diff --git a/README.md b/README.md
index 236f7d4..73d0f0d 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,7 @@ AstraFlow **natively** supports the following for LLM RL training **without any
 <!-- <p align="center"><i>AstraFlow training a multi-policy workflow on an elastic, heterogeneous, cross-region rollout pool — all at once, with no feature-specific code.</i></p> -->
 
 ## News
+- **[2026/07]** AstraFlow **v0.1.2** images released — transformers 5.8 / SGLang 0.5.13 stack, new **Qwen3.5-4B (Gated-DeltaNet)** math recipes, Megatron-image build fix. Both images validated end-to-end on 8×H100 (400-step math-RL runs incl. eval). See `docker/README.md`.
 - **[2026/06]** New recipe: **dynamic recursive agent** on TextCraft — a multi-turn agent that recursively spawns sub-agents sharing inventory under a team reward. See the [recipe docs](https://Infini-AI-Lab.github.io/astraflow/docs/recipes/textcraft-recursive.html).
 - **[2026/06]** AstraFlow **v0.1.1** released — CUDA 13 image, SGLang 0.5.12, Megatron weight-sync training backend, and transformers 5 support. See the [project website](https://Infini-AI-Lab.github.io/astraflow/).
 - **[2026/05]** AstraFlow **v0.1.0** released — first public release of the full system. See the [project website](https://Infini-AI-Lab.github.io/astraflow/).
diff --git a/astraflow/train_worker/api/cli_args.py b/astraflow/train_worker/api/cli_args.py
index 096324e..41b1e6f 100644
--- a/astraflow/train_worker/api/cli_args.py
+++ b/astraflow/train_worker/api/cli_args.py
@@ -473,10 +473,22 @@ class TrainEngineConfig:
     trial_name: str = ""
     path: str = field(default="", metadata={"help": "Path to HuggingFace checkpoint"})
     attn_impl: str = field(
-        default="flash_attention_2",
+        default="kernels-community/flash-attn2",
         metadata={
-            "help": "Attention implementation for huggingface transformers model.",
-            "choices": ["flash_attention_2"],
+            "help": (
+                "Attention implementation for huggingface transformers model. "
+                "Default pulls a prebuilt FlashAttention-2 kernel from the HF kernels "
+                "hub (ABI-matched to torch, incl. varlen for packed sequences). The "
+                "literal 'flash_attention_2' loads the local flash-attn wheel, which is "
+                "ABI-broken on torch>=2.11+cu13; 'sdpa' works but relies on position_ids "
+                "resets for packed block-diagonal masking."
+            ),
+            "choices": [
+                "kernels-community/flash-attn2",
+                "flash_attention_2",
+                "sdpa",
+                "eager",
+            ],
         },
     )
     init_from_scratch: bool = field(
diff --git a/astraflow/train_worker/engine/fsdp_engine.py b/astraflow/train_worker/engine/fsdp_engine.py
index 35c60a5..1385277 100644
--- a/astraflow/train_worker/engine/fsdp_engine.py
+++ b/astraflow/train_worker/engine/fsdp_engine.py
@@ -94,8 +94,6 @@
 from astraflow.train_worker.utils.model import (
     disable_dropout_in_model,
     is_gemma3_model,
-    is_qwen3_moe_model,
-    is_qwen3_vl_model,
     is_qwen_vl_model,
     is_valid_vision_model,
 )
@@ -1206,16 +1204,15 @@ def _prepare_mb_list(self, input_: dict[str, Any]) -> MicroBatchList:
             ]
             mb["use_cache"] = False
             padded_mb["use_cache"] = False
-            if is_qwen3_moe_model(self.model_config.model_type) or is_qwen3_vl_model(
-                self.model_config.model_type
-            ):
-                mb["attention_mask"] = None
-                padded_mb["attention_mask"] = None
-            else:
-                mb["attention_mask"] = dict(full_attention=None, sliding_attention=None)
-                padded_mb["attention_mask"] = dict(
-                    full_attention=None, sliding_attention=None
-                )
+            # Always pass attention_mask=None for the packed/varlen forward: per-sequence
+            # causal masking is driven by cu_seqlens + position_ids, and the model builds
+            # the right mask from None. The old dict(full_attention=None,
+            # sliding_attention=None) form is a transformers-4.x relic: on transformers>=5
+            # a dense model (qwen3 / qwen2) treats that dict as a *precomputed* mask, skips
+            # creation, and crashes. Passing None lets the model build its mask from
+            # cu_seqlens + position_ids instead.
+            mb["attention_mask"] = None
+            padded_mb["attention_mask"] = None
             if "multi_modal_input" in mb:
                 image_grid_thw_list = [
                     item["image_grid_thw"]
diff --git a/astraflow/train_worker/trainer/ppo_trainer.py b/astraflow/train_worker/trainer/ppo_trainer.py
index 16ab8e9..eb775c0 100644
--- a/astraflow/train_worker/trainer/ppo_trainer.py
+++ b/astraflow/train_worker/trainer/ppo_trainer.py
@@ -543,6 +543,33 @@ def train(
                 and getattr(config.evaluator, "freq_steps", None) is not None
             ):
                 if self._is_rank0:
+                    # Gate the pre-train eval on RaaS-pool readiness. The trainer
+                    # reaches loop entry tens of seconds before the SGLang
+                    # inference servers finish starting and register with the
+                    # dataflow pool; firing the eval immediately hits
+                    # "no healthy RaaS instance available for eval" and the v=0
+                    # baseline is silently dropped. Poll /status until at least
+                    # one RaaS instance is registered, then let init settle.
+                    import time as _time
+
+                    _deadline = _time.monotonic() + 300.0
+                    _raas_ready = False
+                    while _time.monotonic() < _deadline:
+                        try:
+                            _pool = self.astraflow.get_status().get("raas_pool", {})
+                            if int(_pool.get("size", 0)) >= 1:
+                                _raas_ready = True
+                                break
+                        except Exception:
+                            pass
+                        _time.sleep(2.0)
+                    if _raas_ready:
+                        _time.sleep(5.0)  # let the just-registered engine finish init
+                    else:
+                        logger.warning(
+                            "[pre-train] RaaS pool not ready after 300s; "
+                            "initial eval may be skipped",
+                        )
                     print(
                         "[Trainer] [pre-train] Running eval at version=0 ...",
                         flush=True,
diff --git a/astraflow/train_worker/utils/model.py b/astraflow/train_worker/utils/model.py
index d7d3d29..491f3d1 100644
--- a/astraflow/train_worker/utils/model.py
+++ b/astraflow/train_worker/utils/model.py
@@ -5,6 +5,9 @@
     "qwen2_vl",
     "qwen2_5_vl",
     "qwen3_vl",
+    # Qwen3.5 dense math checkpoints ship as Qwen3_5ForConditionalGeneration, so they
+    # load via the ImageTextToText path even though these recipes train text-only.
+    "qwen3_5",
     "gemma3",
 ]
 # Registry of vision models verified to work with this framework.
diff --git a/docker/Dockerfile.sglang b/docker/Dockerfile.sglang
index bbc821f..cc44a69 100644
--- a/docker/Dockerfile.sglang
+++ b/docker/Dockerfile.sglang
@@ -29,8 +29,14 @@ RUN uv pip install -e .
 # --- sglang layers (on top of basic) ---
 RUN uv pip install -e ".[sglang]"
 
-# flash-attn is excluded from uv resolution (see pyproject.toml [tool.uv]),
-# so install it explicitly with --no-build-isolation.
-RUN uv pip install "flash-attn==2.8.3" --no-build-isolation
+# flash-attn is excluded from uv's project resolution (pyproject.toml [tool.uv]
+# exclude-dependencies) -- but that exclusion ALSO silently suppresses an
+# explicit `uv pip install flash-attn` when run from the project dir. Install it
+# from /tmp so uv does not read the project's [tool.uv] and actually builds +
+# installs FA2 from source (ABI-matched to the installed torch; builds
+# sm_80/90/100/120 -> Ada/Hopper/Blackwell). Required by the Megatron
+# TransformerEngine `flash` attention backend (THD/packed sequences) and by
+# dense-Qwen3 flash_attention_2.
+RUN cd /tmp && uv pip install "flash-attn==2.8.3" --no-build-isolation
 
 CMD ["/bin/bash"]
diff --git a/docker/Dockerfile.sglang.megatron b/docker/Dockerfile.sglang.megatron
index 24e70a7..51dc8f0 100644
--- a/docker/Dockerfile.sglang.megatron
+++ b/docker/Dockerfile.sglang.megatron
@@ -19,6 +19,24 @@ ENV CUDA_HOME=/usr/local/cuda \
     NVTE_FRAMEWORK=pytorch \
     TORCH_CUDA_ARCH_LIST="8.0;8.9;9.0"
 
+# transformers 5.8 moved `rope_theta` into a nested `config.rope_parameters`
+# dict; mbridge 0.13.0 still reads the old top-level `config.rope_theta` and
+# crashes converting Qwen3 configs to Megatron (AttributeError) at model build.
+# Patch the installed mbridge to read either location. No published mbridge
+# release targets transformers 5.8 yet (AReaL avoids this by staying on
+# transformers <=5.3). mbridge is Megatron-only, so this lives in the Megatron
+# image rather than the FSDP base.
+RUN python - <<'PY'
+p = "/opt/venv/lib/python3.12/site-packages/mbridge/core/llm_bridge.py"
+s = open(p).read()
+old = "rotary_base=self.hf_config.rope_theta,"
+new = ('rotary_base=(getattr(self.hf_config, "rope_theta", None) '
+       'or getattr(self.hf_config, "rope_parameters", {}).get("rope_theta")),')
+assert old in s, "mbridge rope_theta line not found -- patch needs updating"
+open(p, "w").write(s.replace(old, new))
+print("[mbridge] patched rope_theta -> rope_parameters fallback")
+PY
+
 # Transformer Engine. The prebuilt `transformer-engine[pytorch]` wheels link
 # libcublas.so.12 and fail to load on a CUDA 13 base (ImportError:
 # libcublas.so.12), so build TE from source against the image's CUDA 13
diff --git a/docker/README.md b/docker/README.md
index 1ff2709..6f927b6 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -28,17 +28,19 @@ Pick the one that matches your **training backend**:
 
 ```bash
 # FSDP backend (default) — astraflow + SGLang + flash-attn. Covers most recipes.
-docker pull astraflowai/astraflow:v0.1.1
+docker pull astraflowai/astraflow:v0.1.2
 
 # Megatron-LM backend — the above plus Transformer Engine + apex.
 # Only needed when training with `backend: megatron` (TP/PP/EP, MoE, large models).
-docker pull astraflowai/astraflow:v0.1.1.megatron
+docker pull astraflowai/astraflow:v0.1.2.megatron
 ```
 
-`v0.1.1` is built from `Dockerfile.sglang`; `v0.1.1.megatron` from
+`v0.1.2` is built from `Dockerfile.sglang`; `v0.1.2.megatron` from
 `Dockerfile.sglang.megatron`. The Megatron image is a strict superset, so if you are
 unsure it also runs every FSDP recipe. Pin a version tag for reproducibility;
-`:latest` tracks the most recent FSDP release.
+`:latest` tracks the most recent FSDP release. Both `v0.1.2` images are validated
+end-to-end on 8×H100 (400-step math-RL runs incl. eval; FSDP with Qwen3.5-4B and
+dense Qwen3-8B, Megatron with Qwen3-8B).
 
 ## Build from source
 
@@ -55,12 +57,36 @@ docker build -f docker/Dockerfile.sglang.megatron -t astraflow:sglang-megatron .
 
 ## Quick Start
 
+The recommended workflow: **the image provides the environment** (Python venv,
+CUDA 13 toolkit, SGLang, flash-attn, fla kernels); **your local checkout provides
+the code**. astraflow is installed *editable* from `/workspace/astraflow`, so
+mounting your repo over that path makes the container run your code — code
+changes take effect immediately, and you only rebuild the image when the
+*environment* changes (dependency pins, CUDA, system libs).
+
+```bash
+# Train with YOUR local checkout inside the pre-built environment
+docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 \
+  -v /path/to/astraflow:/workspace/astraflow \
+  -v ~/.cache/huggingface:/hf -e HF_HOME=/hf \
+  -e WANDB_API_KEY=<your-key> \
+  astraflowai/astraflow:v0.1.2 \
+  bash examples/math/qwen3-8b-m2po-full/scripts/run_qwen3-8b-m2po-full.sh
+```
+
+- `-v /path/to/astraflow:/workspace/astraflow` — your repo replaces the baked-in
+  code (outputs land in `data-experiments/`/`data-log/` inside your checkout).
+- `-v ~/.cache/huggingface:/hf -e HF_HOME=/hf` — reuse your host model/dataset
+  cache instead of re-downloading inside the container.
+
+To poke around the image with its baked-in code instead (no mounts), start an
+interactive shell:
+
 ```bash
-# Run the pre-built FSDP image with host network and all GPUs
-docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.1
+docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.2
 
 # ...or the Megatron-backend image
-docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.1.megatron
+docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.2.megatron
 
 # ...or run a locally built image
 docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflow:sglang
diff --git a/docs/en/get-started/installation.md b/docs/en/get-started/installation.md
index c322632..6d2a7aa 100644
--- a/docs/en/get-started/installation.md
+++ b/docs/en/get-started/installation.md
@@ -28,7 +28,7 @@ pip install -U "uv>=0.10"
 > (`extra-build-dependencies`, `override-dependencies`) that older uv
 > releases don't recognize. When uv hits an unknown `[tool.uv]` key it
 > silently ignores the *entire* `[tool.uv]` table, so the
-> `transformers==5.6.1` override (which must beat sglang's `==5.6.0` pin)
+> `transformers==5.8.1` override (the version the pinned sglang release requires)
 > is dropped and the install fails with an unsolvable
 > `transformers` conflict. The Docker images install the latest uv via the
 > official installer and are unaffected.
@@ -40,7 +40,7 @@ uv pip install -e ".[dev]"
 ```
 
 This installs all core dependencies (~260 packages) including PyTorch 2.11.0,
-Transformers 5.6.1, Megatron-Core 0.13.1, Ray, W&B, and dev tools (pytest, ruff,
+Transformers 5.8.1, Megatron-Core 0.13.1, Ray, W&B, and dev tools (pytest, ruff,
 ipython).
 
 ### Step 4: Install Flash Attention and SGLang
@@ -72,7 +72,7 @@ uv pip install "flash-attn==2.8.3" --no-build-isolation
 #### SGLang (inference backend)
 
 Install via the project extra so uv applies the `[tool.uv]` overrides (the
-`transformers==5.6.1` pin and the `flash-attn-4` pre-release allowance). SGLang
+`transformers==5.8.1` pin and the `flash-attn-4` pre-release allowance). SGLang
 pulls in FlashAttention-**4** (`flash-attn-4`, a pre-release wheel) automatically
 for its own attention backend — you do not install that one yourself.
 
@@ -87,7 +87,7 @@ pipeline / expert parallelism, MoE models). The default **FSDP** backend and
 all inference need nothing here — skip to Step 6.
 
 > **Prefer Docker?** Skip this entire step with the pre-built
-> `astraflowai/astraflow:v0.1.1.megatron` image (see Option B below), which already
+> `astraflowai/astraflow:v0.1.2.megatron` image (see Option B below), which already
 > bundles Transformer Engine + apex.
 
 `megatron-core` and `mbridge` are already installed by Step 3. The Megatron
@@ -167,15 +167,30 @@ Pre-built images are published on Docker Hub — they skip the from-source steps
 entirely. Requires the NVIDIA Container Toolkit so `--gpus all` works. Choose the image
 by **training backend**:
 
+The recommended workflow: **the image provides the environment, your local
+checkout provides the code**. astraflow is installed *editable* from
+`/workspace/astraflow`, so mounting your repo over that path runs your local
+code — no rebuild needed for code changes; rebuild only when dependency pins or
+the CUDA stack change.
+
 ```bash
-# FSDP backend (default) — covers most recipes
-docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.1
+# FSDP backend (default) — run a recipe from YOUR local checkout
+docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 \
+  -v /path/to/astraflow:/workspace/astraflow \
+  -v ~/.cache/huggingface:/hf -e HF_HOME=/hf \
+  -e WANDB_API_KEY=<your-key> \
+  astraflowai/astraflow:v0.1.2 \
+  bash examples/math/qwen3-8b-m2po-full/scripts/run_qwen3-8b-m2po-full.sh
 
 # Megatron-LM backend — adds Transformer Engine + apex (Step 5 above, pre-built in).
 # Use this for `backend: megatron` (TP/PP/EP, MoE, large models).
-docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.1.megatron
+docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it astraflowai/astraflow:v0.1.2.megatron
 ```
 
+Drop the `-v /path/to/astraflow:...` mount to use the code baked into the image
+instead; add `-it` for an interactive shell. The `-v ~/.cache/huggingface` mount
+reuses your host model/dataset cache instead of re-downloading in the container.
+
 > **Note on `--shm-size`:** this sets the size of the container's `/dev/shm`. A
 > recipe run co-locates the trainer, RaaS, and SGLang in a single container, all
 > sharing one `/dev/shm` — in particular RaaS stages received model weights under
@@ -191,6 +206,6 @@ docker run --gpus all --net=host --shm-size=512g --ulimit nofile=65536:65536 -it
 > `nofile` soft limit (1024) is far too low and the reward pool fails with
 > `[Errno 24] Too many open files`. Raise it with `--ulimit nofile=65536:65536`.
 
-The image bundles astraflow, SGLang, and flash-attn. Pin a version tag (`v0.1.1`) for
+The image bundles astraflow, SGLang, and flash-attn. Pin a version tag (`v0.1.2`) for
 reproducibility; `:latest` tracks the most recent release. See `docker/README.md` for
 build details and the NVIDIA Container Toolkit install guide.
diff --git a/docs/en/recipes/math.md b/docs/en/recipes/math.md
index 9d1a5d9..6542c20 100644
--- a/docs/en/recipes/math.md
+++ b/docs/en/recipes/math.md
@@ -43,6 +43,47 @@ bash examples/math/qwen3-1.7b-m2po-2gpus-full/scripts/run_qwen3-1.7b-m2po-2gpus-
 | Train dataset | DeepScaleR |
 | Eval datasets | AIME24, AIME25, AMC, Minerva Math, MATH500 |
 
+## Qwen3.5-4B — 8 GPUs (hybrid Gated-DeltaNet)
+
+Math RL on **Qwen3.5-4B**, a hybrid Gated-DeltaNet + attention multimodal
+checkpoint trained text-only here. Requires the transformers 5.8 stack
+(`transformers==5.8.1`, `sglang==0.5.13.post1`, `fla` kernels — all pinned by
+`pyproject.toml`, or use the `astraflowai/astraflow:v0.1.2` image). Full and
+delta transfer variants:
+
+- [`qwen3.5-4b-m2po-full/`](https://github.com/Infini-AI-Lab/astraflow/tree/main/examples/math/qwen3.5-4b-m2po-full) — full weight transfer
+- [`qwen3.5-4b-m2po-delta/`](https://github.com/Infini-AI-Lab/astraflow/tree/main/examples/math/qwen3.5-4b-m2po-delta) — delta weight transfer
+
+### Run
+
+```bash
+bash examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh
+```
+
+### Settings
+
+| Setting | Value |
+|---|---|
+| Model | Qwen3.5-4B (`model_type: qwen3_5`, GDN hybrid) |
+| GPUs | 8 — RaaS ×4 (SGLang, DP=4), Trainer ×4 (FSDP, DP=4) |
+| Backend | **FSDP only** — Qwen3.5 is not supported on the Megatron backend (no `mbridge` bridge / GDN layer spec) |
+| Algorithm | M2PO (`m2_threshold` 0.01) |
+| Weight transfer | TCP — full or delta |
+| Context length | 8192 |
+| Max new tokens | 4000 |
+| Rollouts per prompt | 8 (`temperature` 1.0) |
+| Train batch size | 256 |
+| Learning rate | 5e-6 (Adam, constant schedule) |
+| Train steps | 800 |
+| Workflow / reward | `rlvr` / `math_verify` |
+| Train dataset | DeepScaleR |
+| Eval datasets | AIME24, AIME25, AMC, Minerva Math, MATH500 |
+
+Validated on 8×L40 and 8×H100 (on Hopper the launch scripts auto-set the
+`FLA_TILELANG`/`CUDA_HOME` environment the GDN backward needs). See the
+[recipe README](https://github.com/Infini-AI-Lab/astraflow/tree/main/examples/math/qwen3.5-4b-m2po-full)
+for the validated stack and results.
+
 ## Qwen3-8B — 8 GPUs
 
 The full-scale recipe. It needs an 8-GPU node — 4 GPUs for inference, 4 for training — and also comes in full and delta transfer variants:
diff --git a/examples/_common/utils.sh b/examples/_common/utils.sh
index 130e532..d3dd95b 100644
--- a/examples/_common/utils.sh
+++ b/examples/_common/utils.sh
@@ -108,3 +108,20 @@ astraflow_cleanup_trap() {
     wait 2>/dev/null
     exit 0
 }
+
+# Qwen3.5 (Gated-DeltaNet) on Hopper: the GDN backward must use fla's tilelang
+# kernel (fla blocks its triton path on sm_90 as numerically wrong, fla#640),
+# and the tilelang JIT needs a full CUDA toolkit (nvcc + CCCL headers; the
+# pip-shipped nvcc has none). Called by the Qwen3.5 recipe launchers before
+# starting the trainer. No-op on non-Hopper GPUs and when the user has already
+# set the variables.
+astraflow_setup_qwen35_hopper_env() {
+    if nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null | grep -q '^9\.'; then
+        export FLA_TILELANG="${FLA_TILELANG:-1}"
+        if [ -z "${CUDA_HOME:-}" ] && [ -d /usr/local/cuda ]; then
+            export CUDA_HOME=/usr/local/cuda
+            export PATH="${CUDA_HOME}/bin:${PATH}"
+        fi
+        echo "Hopper (sm_90) detected: FLA_TILELANG=${FLA_TILELANG} CUDA_HOME=${CUDA_HOME:-unset}"
+    fi
+}
diff --git a/examples/math/README.md b/examples/math/README.md
index 0b2735e..cc4a81f 100644
--- a/examples/math/README.md
+++ b/examples/math/README.md
@@ -15,3 +15,21 @@ Complete guidance: [`docs/en/recipes/math.md`](../../docs/en/recipes/math.md).
 
 Most math recipes default to one 8xH100 node. The `qwen3-1.7b-m2po-2gpus-*`
 recipes are smaller 2xH100 variants.
+
+---
+**Attention kernel**
+
+The dense Qwen3 recipes (`qwen3-1.7b-m2po-2gpus-*`, `qwen3-8b-m2po-*`) set
+`attn_impl: kernels-community/flash-attn2` — a prebuilt, ABI-matched
+FlashAttention-2 kernel pulled from the Hugging Face `kernels` hub (fetched and
+cached on first use; no source build). This is the working FA2 on the validated
+stack (`torch 2.11+cu130`): the literal `attn_impl: flash_attention_2` would
+instead load the local `flash-attn` wheel and crash with an `undefined symbol`
+ABI error (`is_flash_attn_2_available()` is metadata-only, so it never catches
+the broken import). It is also the same kernel as `cli_args.py`'s default, so
+recipes that omit `attn_impl` get it too.
+
+`sdpa` and `eager` remain available; `sdpa` works but relies on per-sequence
+`position_ids` resets for packed block-diagonal masking, whereas FA2 varlen
+derives the block-diagonal mask from `cu_seqlens` directly. The Qwen3.5 recipes
+use `sdpa` (hybrid Gated-DeltaNet + attention model).
diff --git a/examples/math/qwen3-1.7b-m2po-2gpus-delta/yaml/experiment.yaml b/examples/math/qwen3-1.7b-m2po-2gpus-delta/yaml/experiment.yaml
index e758135..f1a0a27 100644
--- a/examples/math/qwen3-1.7b-m2po-2gpus-delta/yaml/experiment.yaml
+++ b/examples/math/qwen3-1.7b-m2po-2gpus-delta/yaml/experiment.yaml
@@ -110,6 +110,7 @@ trainer_base:
     data_parallel_size: 1
 
   actor:
+    attn_impl: kernels-community/flash-attn2
     gradient_checkpointing: true
     mb_spec:
       max_tokens_per_mb: 17408
@@ -135,6 +136,7 @@ trainer_base:
     adv_norm: { mean_level: batch, std_level: batch }
 
   ref:
+    attn_impl: kernels-community/flash-attn2
     mb_spec:
       max_tokens_per_mb: 17408
 
diff --git a/examples/math/qwen3-1.7b-m2po-2gpus-full/yaml/experiment.yaml b/examples/math/qwen3-1.7b-m2po-2gpus-full/yaml/experiment.yaml
index 766c242..1a8cd02 100644
--- a/examples/math/qwen3-1.7b-m2po-2gpus-full/yaml/experiment.yaml
+++ b/examples/math/qwen3-1.7b-m2po-2gpus-full/yaml/experiment.yaml
@@ -109,6 +109,7 @@ trainer_base:
     data_parallel_size: 1
 
   actor:
+    attn_impl: kernels-community/flash-attn2
     gradient_checkpointing: true
     mb_spec:
       max_tokens_per_mb: 17408
@@ -134,6 +135,7 @@ trainer_base:
     adv_norm: { mean_level: batch, std_level: batch }
 
   ref:
+    attn_impl: kernels-community/flash-attn2
     mb_spec:
       max_tokens_per_mb: 17408
 
diff --git a/examples/math/qwen3-8b-m2po-delta/yaml/experiment.yaml b/examples/math/qwen3-8b-m2po-delta/yaml/experiment.yaml
index 1614b93..1629c36 100644
--- a/examples/math/qwen3-8b-m2po-delta/yaml/experiment.yaml
+++ b/examples/math/qwen3-8b-m2po-delta/yaml/experiment.yaml
@@ -110,6 +110,7 @@ trainer_base:
     data_parallel_size: 4
 
   actor:
+    attn_impl: kernels-community/flash-attn2
     gradient_checkpointing: true
     mb_spec:
       max_tokens_per_mb: 17408
@@ -135,6 +136,7 @@ trainer_base:
     adv_norm: { mean_level: batch, std_level: batch }
 
   ref:
+    attn_impl: kernels-community/flash-attn2
     mb_spec:
       max_tokens_per_mb: 17408
 
diff --git a/examples/math/qwen3-8b-m2po-full/yaml/experiment.yaml b/examples/math/qwen3-8b-m2po-full/yaml/experiment.yaml
index f00f8d2..8fa6c90 100644
--- a/examples/math/qwen3-8b-m2po-full/yaml/experiment.yaml
+++ b/examples/math/qwen3-8b-m2po-full/yaml/experiment.yaml
@@ -109,6 +109,7 @@ trainer_base:
     data_parallel_size: 4
 
   actor:
+    attn_impl: kernels-community/flash-attn2
     gradient_checkpointing: true
     mb_spec:
       max_tokens_per_mb: 17408
@@ -134,6 +135,7 @@ trainer_base:
     adv_norm: { mean_level: batch, std_level: batch }
 
   ref:
+    attn_impl: kernels-community/flash-attn2
     mb_spec:
       max_tokens_per_mb: 17408
 
diff --git a/examples/math/qwen3-8b-megatron-delta/README.md b/examples/math/qwen3-8b-megatron-delta/README.md
index 2cc2593..0cfb885 100644
--- a/examples/math/qwen3-8b-megatron-delta/README.md
+++ b/examples/math/qwen3-8b-megatron-delta/README.md
@@ -65,3 +65,12 @@ sizes in the `engine` block, e.g. `pipeline_parallel_size: 2` or
 `expert_parallel_size: 2`. The backend auto-selects Megatron when `pp>1` or
 `ep>1`. Ensure `data_parallel_size * tensor_parallel_size *
 pipeline_parallel_size` equals the number of trainer GPUs.
+
+## Supported models
+
+The Megatron backend converts HF checkpoints via `mbridge`, which supports these
+architectures: Qwen2 / Qwen2-MoE / Qwen2.5-VL / Qwen3 / Qwen3-MoE / Llama /
+Mixtral / DeepSeek-V3 / MiMo. **Qwen3.5 (`model_type: qwen3_5`) is not supported
+on Megatron:** it is a Gated-DeltaNet hybrid, and neither `mbridge` (no `qwen3_5`
+bridge) nor Megatron-Core (no GDN layer spec) can build or weight-load it. Train
+Qwen3.5 on the FSDP backend instead — see `examples/math/qwen3.5-4b-m2po-full`.
diff --git a/examples/math/qwen3.5-4b-m2po-delta/README.md b/examples/math/qwen3.5-4b-m2po-delta/README.md
new file mode 100644
index 0000000..1fc3672
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/README.md
@@ -0,0 +1,20 @@
+# Qwen3.5-4B — Math RL (M2PO), delta weight transfer
+
+Same recipe as [`qwen3.5-4b-m2po-full`](../qwen3.5-4b-m2po-full/README.md), but
+the trainer pushes **only changed weights** to the inference engine each sync
+(`weight_transfer_strategies: delta`) instead of the full model.
+
+See the [full recipe's README](../qwen3.5-4b-m2po-full/README.md) for the
+validated environment (transformers 5.8.1 / kernels 0.14.1 / SGLang
+`0.5.13.post1` with `qwen3_5`, `attention_backend: flashinfer` / `fla` 0.5.0 /
+torch 2.11.0+cu130),
+GPU layout, install note, **backend support (FSDP only — Qwen3.5 is not
+supported on the Megatron backend)**, the **Hopper/H100 note** (the trainer
+script auto-sets `FLA_TILELANG=1` + `CUDA_HOME` on sm_90 for the GDN backward),
+and validation results.
+
+## Run
+
+```bash
+bash examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh
+```
diff --git a/examples/math/qwen3.5-4b-m2po-delta/scripts/1_astraflow.sh b/examples/math/qwen3.5-4b-m2po-delta/scripts/1_astraflow.sh
new file mode 100755
index 0000000..851e565
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/scripts/1_astraflow.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+set -euo pipefail
+# [1/3] Launch AstraFlow HTTP service
+#
+# Usage (terminal 1):
+#   bash examples/math/qwen3.5-4b-m2po-delta/scripts/1_astraflow.sh
+
+export CUDA_VISIBLE_DEVICES=""
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export ASTRAFLOW_HOST="${ASTRAFLOW_HOST:-0.0.0.0}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+echo "=== AstraFlow HTTP Service ==="
+echo "Experiment config : ${EXPERIMENT_CONFIG}"
+echo "Port              : ${ASTRAFLOW_PORT}"
+echo "==============================="
+
+python3 -u -m astraflow \
+  --config "${EXPERIMENT_CONFIG}" \
+  --port "${ASTRAFLOW_PORT}" \
+  --host "${ASTRAFLOW_HOST}" \
+  2>&1 | tee "${LOG_DIR}/astraflow.log"
diff --git a/examples/math/qwen3.5-4b-m2po-delta/scripts/2_raas.sh b/examples/math/qwen3.5-4b-m2po-delta/scripts/2_raas.sh
new file mode 100755
index 0000000..b9cfe79
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/scripts/2_raas.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+set -euo pipefail
+# [2/3] Launch RaaS inference server (SGLang + TCP receiver)
+#
+# Usage (terminal 2, after AstraFlow is ready):
+#   bash examples/math/qwen3.5-4b-m2po-delta/scripts/2_raas.sh
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+export RAAS_CONFIG="${RAAS_CONFIG:-${YAML_DIR}/raas.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES:-0,1,2,3}"
+export RAAS_HOST="${RAAS_HOST:-0.0.0.0}"
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="${ASTRAFLOW_URL:-http://127.0.0.1:${ASTRAFLOW_PORT}}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+echo "=== RaaS Inference Server (SGLang + TCP receiver) ==="
+echo "Experiment config : ${EXPERIMENT_CONFIG}"
+echo "RaaS config       : ${RAAS_CONFIG}"
+echo "GPUs              : ${CUDA_VISIBLE_DEVICES}"
+echo "Port              : ${RAAS_PORT}"
+echo "AstraFlow URL     : ${ASTRAFLOW_URL}"
+echo "======================================================="
+
+python3 -u -m astraflow.raas.server \
+  --host "${RAAS_HOST}" \
+  --port "${RAAS_PORT}" \
+  --config "${EXPERIMENT_CONFIG}" \
+  --config "${RAAS_CONFIG}" \
+  --engine-id "${ENGINE_ID:-default}" \
+  --astraflow-url "${ASTRAFLOW_URL}" \
+  2>&1 | tee "${LOG_DIR}/raas.log"
diff --git a/examples/math/qwen3.5-4b-m2po-delta/scripts/3_trainer_model0.sh b/examples/math/qwen3.5-4b-m2po-delta/scripts/3_trainer_model0.sh
new file mode 100755
index 0000000..4ee2834
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/scripts/3_trainer_model0.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+set -euo pipefail
+# [3/3] Launch Trainer for model0 (TCP, sender_agent on local_rank 0)
+#
+# Usage (terminal 3, after AstraFlow and RaaS are ready):
+#   bash examples/math/qwen3.5-4b-m2po-delta/scripts/3_trainer_model0.sh
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export CUDA_VISIBLE_DEVICES="${TRAINER_MODEL0_GPUS:-4,5,6,7}"
+TRAINER0_NPROC="$(echo "${CUDA_VISIBLE_DEVICES}" | awk -F',' '{print NF}')"
+
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="http://127.0.0.1:${ASTRAFLOW_PORT}"
+export ASTRAFLOW_RAAS_URL="http://127.0.0.1:${RAAS_PORT}"
+
+# sender_agent (in trainer) listens on this HTTP port
+export WEIGHT_TRANSFER_HTTP_PORT="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0:-19861}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+# Hopper needs FLA_TILELANG + CUDA_HOME for the GDN backward (see utils.sh).
+astraflow_setup_qwen35_hopper_env
+
+echo "=== Trainer model0 (TCP) ==="
+echo "Experiment config   : ${EXPERIMENT_CONFIG}"
+echo "GPUs                : ${CUDA_VISIBLE_DEVICES} (FSDP dp${TRAINER0_NPROC})"
+echo "AstraFlow           : ${ASTRAFLOW_URL}"
+echo "RaaS                : ${ASTRAFLOW_RAAS_URL}"
+echo "Sender HTTP         : ${WEIGHT_TRANSFER_HTTP_PORT}"
+echo "WANDB mode          : ${WANDB_MODE:-online}"
+echo "=========================================="
+
+torchrun --nnodes 1 --nproc-per-node "${TRAINER0_NPROC}" \
+  --master-addr "${MASTER_ADDR:-127.0.0.1}" --master-port "${MASTER_PORT_MODEL0:-29541}" \
+  examples/launch_trainer.py \
+  --config "${EXPERIMENT_CONFIG}" \
+  --trainer trainer_model0 \
+  "$@" 2>&1 | tee "${LOG_DIR}/trainer_model0.log"
diff --git a/examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh b/examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh
new file mode 100755
index 0000000..42b66fa
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh
@@ -0,0 +1,110 @@
+#!/bin/bash
+set -euo pipefail
+# All-in-one launcher for AstraFlow v2 math training (Qwen3.5-4B, M2PO, TCP).
+#
+# Launches 3 processes:
+#   1. AstraFlow HTTP service (CPU-only)
+#   2. RaaS inference server  (SGLang, SERVICE_CUDA_VISIBLE_DEVICES)
+#   3. Trainer model0         (math, TRAINER_MODEL0_GPUS)
+#
+# Requires: transformers>=5.8 (+ flash-linear-attention for training),
+#           SGLang main (qwen3_5 model). See yaml/raas.yaml for the backend note.
+#
+# Usage:
+#   bash examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh
+
+# =============================================================================
+# Part 1: Load env and settings
+# =============================================================================
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+export RAAS_CONFIG="${RAAS_CONFIG:-${YAML_DIR}/raas.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+# Defined in examples/_common/utils.sh.
+astraflow_load_experiment_env
+
+# =============================================================================
+# Part 2: Set up env
+# =============================================================================
+# GPU assignments (default: 4 GPUs for inference, 4 for training)
+export SERVICE_CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES:-0,1,2,3}"
+export TRAINER_MODEL0_GPUS="${TRAINER_MODEL0_GPUS:-4,5,6,7}"
+# Ports / URLs (each component gets its own port)
+export RAAS_HOST="${RAAS_HOST:-0.0.0.0}"
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_HOST="${ASTRAFLOW_HOST:-0.0.0.0}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="http://127.0.0.1:${ASTRAFLOW_PORT}"
+export WEIGHT_TRANSFER_HTTP_PORT_MODEL0="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0:-19861}"
+
+TRAINER0_NPROC="$(echo "${TRAINER_MODEL0_GPUS}" | awk -F',' '{print NF}')"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR.
+# Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+# =============================================================================
+# Part 3: Print info and clean up
+# =============================================================================
+echo "=== AstraFlow (Qwen3.5-4B, math, M2PO, ctx8k, TCP delta) ==="
+echo "Experiment config   : ${EXPERIMENT_CONFIG}"
+echo "RaaS config         : ${RAAS_CONFIG}"
+echo "RaaS GPUs           : ${SERVICE_CUDA_VISIBLE_DEVICES}"
+echo "Trainer model0 GPUs : ${TRAINER_MODEL0_GPUS} (FSDP dp${TRAINER0_NPROC})"
+echo "RaaS port           : ${RAAS_PORT}"
+echo "AstraFlow port      : ${ASTRAFLOW_PORT}"
+echo "Sender HTTP model0  : ${WEIGHT_TRANSFER_HTTP_PORT_MODEL0}"
+echo "WANDB mode          : ${WANDB_MODE:-online}"
+echo "=========================================================="
+
+trap astraflow_cleanup_trap EXIT INT TERM
+
+# Kill leftover processes and shared memory from prior runs.
+# Defined in examples/_common/utils.sh.
+astraflow_kill_stale
+
+# =============================================================================
+# Part 4: Launch training
+# =============================================================================
+echo "[1/3] Starting AstraFlow HTTP service..."
+CUDA_VISIBLE_DEVICES="" \
+  python3 -u -m astraflow \
+    --config "${EXPERIMENT_CONFIG}" \
+    --port "${ASTRAFLOW_PORT}" \
+    --host "${ASTRAFLOW_HOST}" \
+    2>&1 | tee "${LOG_DIR}/astraflow.log" &
+sleep 5
+
+echo "[2/3] Starting RaaS inference server (SGLang + TCP receiver)..."
+CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES}" \
+  python3 -u -m astraflow.raas.server \
+    --host "${RAAS_HOST}" \
+    --port "${RAAS_PORT}" \
+    --config "${EXPERIMENT_CONFIG}" \
+    --config "${RAAS_CONFIG}" \
+    --engine-id "${ENGINE_ID:-default}" \
+    --astraflow-url "${ASTRAFLOW_URL}" \
+    2>&1 | tee "${LOG_DIR}/raas.log" &
+sleep 15
+
+export ASTRAFLOW_RAAS_URL="http://127.0.0.1:${RAAS_PORT}"
+
+# Hopper needs FLA_TILELANG + CUDA_HOME for the GDN backward (see utils.sh).
+astraflow_setup_qwen35_hopper_env
+
+echo "[3/3] Starting trainer model0..."
+CUDA_VISIBLE_DEVICES="${TRAINER_MODEL0_GPUS}" \
+WEIGHT_TRANSFER_HTTP_PORT="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0}" \
+  torchrun --nnodes 1 --nproc-per-node "${TRAINER0_NPROC}" \
+    --master-addr "${MASTER_ADDR:-127.0.0.1}" --master-port "${MASTER_PORT_MODEL0:-29541}" \
+    examples/launch_trainer.py \
+    --config "${EXPERIMENT_CONFIG}" \
+    --trainer trainer_model0 \
+    "$@" \
+    2>&1 | tee "${LOG_DIR}/trainer_model0.log"
diff --git a/examples/math/qwen3.5-4b-m2po-delta/yaml/experiment.yaml b/examples/math/qwen3.5-4b-m2po-delta/yaml/experiment.yaml
new file mode 100644
index 0000000..60cbbf3
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/yaml/experiment.yaml
@@ -0,0 +1,170 @@
+# ============================================================================
+# Experiment config — AstraFlow service + Trainer
+# Experiment: math / qwen3.5-4b-m2po-delta
+#
+# Qwen3.5-4B math RL with M2PO, ctx 8k, lr 5e-6, delta TCP weight transfer.
+#
+# NOTE: Qwen3.5-4B is a HYBRID (Gated-DeltaNet + attention) multimodal model
+#   (architecture Qwen3_5ForConditionalGeneration, model_type qwen3_5), trained
+#   here TEXT-ONLY for math RL. Requires transformers>=5.8 (+ `fla` kernels for
+#   training) and SGLang main (qwen3_5 + TritonGDNKernel) for inference.
+#   attn_impl=sdpa (prebuilt flash-attn is not ABI-compatible with this torch).
+#
+# GPU layout (default, 8 GPUs):
+#   SERVICE_CUDA_VISIBLE_DEVICES=0,1,2,3 -> RaaS (model0 dp=4)
+#   TRAINER_MODEL0_GPUS=4,5,6,7          -> Trainer model0 (FSDP, 4 GPUs)
+# ============================================================================
+
+# ── Experiment: identity, model, shared settings ──
+experiment:
+  experiment_name: astraflow-math
+  trial_name: qwen3.5-4b-m2po-delta
+  fileroot: ./data-experiments/${experiment.experiment_name}/${experiment.trial_name}
+
+  model_path: "Qwen/Qwen3.5-4B"
+  tokenizer_path: "Qwen/Qwen3.5-4B"
+  seed: 1
+  dtype: bfloat16
+  weight_transfer_mode: tcp
+  weight_transfer_strategies: delta
+
+# ── RaaS: what to generate (inference-level config) ──
+# model keys here also determine expected_model_ids for AstraFlow service
+raas:
+  models:
+    model0:
+      backend: sglang
+      gconfig:
+        n_samples: 8
+        temperature: 1.0
+        max_new_tokens: 4000
+        min_new_tokens: 0
+
+# ── AstraFlow: data pipeline ──
+# auto-derives: expected_model_ids from raas.models keys
+# auto-derives: dump_dir from experiment.fileroot
+dataflow:
+  host: "0.0.0.0"
+  port: 8000
+  delta_full_sync_interval: 10
+
+  buffer:
+    size: 10000
+    replay_size: 10000
+    replay_ratio: 0
+    max_staleness: 8
+    filter_function: filter_zero_adv
+
+  rollout_dataset:
+    dataset_fn: "astraflow.dataflow.dataset.deepscaler:get_deepscaler_rl_dataset"
+    max_length: 2000
+
+  workflow_spec:
+    workflow_cls: "rlvr"
+    reward_fn: "math_verify"
+    enable_thinking: false
+
+  eval_workflows:
+    math_eval:
+      workflow_cls: "rlvr"
+      reward_fn: "math_verify"
+      enable_thinking: false
+      gconfig_overrides:
+        temperature: 0.6
+        n_samples: 1
+
+  eval_datasets:
+    aime24:
+      dataset_fn: "astraflow.dataflow.dataset.aime24x4:get_aime_2024x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    aime25:
+      dataset_fn: "astraflow.dataflow.dataset.aime25x4:get_aime_2025x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    amc:
+      dataset_fn: "astraflow.dataflow.dataset.amc24:get_amc_2024x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    minerva:
+      dataset_fn: "astraflow.dataflow.dataset.minervamath:get_minerva_math_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    math500:
+      dataset_fn: "astraflow.dataflow.dataset.math500:get_math500_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+
+# ── Trainer base: shared config ──
+# auto-derives from experiment: experiment_name, trial_name, fileroot,
+#   tokenizer_path, seed, dtype, weight_transfer_mode
+# auto-derives from raas.models.<model_id>: actor.path, actor.max_new_tokens,
+#   ref.path
+# auto-derives: saver, recover, stats_logger fields from experiment section
+# auto-derives: cluster.name_resolve from experiment.fileroot
+# auto-derives: trial_name suffix from model_id (e.g. trial_name-model0)
+trainer_base:
+  total_train_steps: 800
+  train_batch_size: 256
+  n_samples: 8
+  engine:
+    backend: fsdp
+    data_parallel_size: 4
+
+  actor:
+    # sdpa: prebuilt flash-attn isn't ABI-compatible with this torch/cu130 build;
+    # Qwen3.5's GDN linear-attn uses fla kernels and full-attn blocks use sdpa.
+    attn_impl: sdpa
+    gradient_checkpointing: true
+    mb_spec:
+      max_tokens_per_mb: 8192
+    optimizer:
+      type: adam
+      lr: 5e-6
+      weight_decay: 0.01
+      beta1: 0.9
+      beta2: 0.999
+      eps: 1e-8
+      lr_scheduler_type: constant
+      gradient_clipping: 1.0
+    # PPO / M2PO algorithm
+    m2_threshold: 0.01
+    eps_clip: 100.0
+    eps_clip_higher: 100.0
+    reward_scaling: 1
+    reward_bias: 0
+    kl_ctl: 0.00
+    kl_penalty_coef: 0.001
+    ppo_n_minibatches: 4
+    reward_norm: { mean_level: group, std_level: group }
+    adv_norm: { mean_level: batch, std_level: batch }
+
+  ref:
+    attn_impl: sdpa
+    mb_spec:
+      max_tokens_per_mb: 8192
+
+  recover:
+    mode: auto
+    freq_steps: 25
+
+  evaluator:
+    eval_at_start: false
+    freq_steps: 25
+
+  stats_logger:
+    wandb:
+      mode: online
+      id_suffix: "uid"
+
+# ── Trainer for model0 — only overrides ──
+trainer_model0:
+  model_id: model0
+  stats_logger:
+    wandb:
+      tags: ["m2po", "math", "astraflow-v2", "qwen3.5-4b", "tcp", "ctx8k", "hybrid-gdn", "delta"]
diff --git a/examples/math/qwen3.5-4b-m2po-delta/yaml/raas.yaml b/examples/math/qwen3.5-4b-m2po-delta/yaml/raas.yaml
new file mode 100644
index 0000000..d8bb9fe
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-delta/yaml/raas.yaml
@@ -0,0 +1,52 @@
+# ============================================================================
+# RaaS config — Inference serving instance (hardware/resources)
+# Experiment: math / qwen3.5-4b-m2po-delta
+#
+# Hardware: 4x GPU, TP=1
+#   model0:  DP=4, TP=1
+#
+# Qwen3.5-4B is a hybrid Gated-DeltaNet model: SGLang allocates a Mamba/SSM
+# state cache (~10 GB) in addition to the KV cache, so keep mem_fraction_static
+# conservative. Served by SGLang main via its TritonGDNKernel backend.
+#
+# Merged with experiment.yaml at launch (--config experiment.yaml --config raas.yaml)
+# ============================================================================
+
+rollout:
+  max_concurrent_rollouts: 512
+  # Cap concurrent eval prefills to bound peak KV pressure during the
+  # ~3.5k-item eval burst (5 datasets x repeat=4) — default 128 OOMs sglang.
+  max_concurrent_evals: 64
+  pause_grace_period: 3
+  # Adaptive availability — drive /availability off sglang /get_load.
+  enable_adaptive_availability: true
+  target_waiting_queue_per_dp: 4
+  adaptive_step_size: 4
+  load_cache_ttl_ms: 100
+
+engine:
+  model0:
+    backend: sglang
+    data_parallel_size: 4
+
+sglang:
+  context_length: 8192
+  mem_fraction_static: 0.7
+  # Attention backend. NOTE: this is specific to Qwen3.5's HYBRID
+  # Gated-DeltaNet architecture, NOT a general L40 limitation:
+  #   - Plain dense Qwen3 (full-attention) runs fine with the default fa3 on
+  #     L40/Ada. But for Qwen3.5's GDN path, fa3 dispatches a Hopper-only
+  #     kernel (hopper/flash_fwd_launch_template.h) that fails on Ada (sm_89)
+  #     with "CUDA error: invalid argument" under real load.
+  #   - On non-Hopper archs SGLang auto-selects flashinfer (full-attn) +
+  #     triton (the GDN/linear-attn + mamba layers). Both flashinfer and
+  #     triton are verified working for Qwen3.5-4B here; flashinfer is set
+  #     explicitly (SGLang's literal auto-default on Ada/L40). triton is an
+  #     equally-valid alternative (set attention_backend: triton).
+  attention_backend: flashinfer
+  # Cap concurrency: with n_samples=8 x max_new_tokens=4000 and an
+  # unbounded queue, the hybrid GDN's KV + Mamba state cache overflows
+  # -> 'KV cache pool full, retract' -> CUDA OOM on a 44GB L40. 32 keeps
+  # peak KV bounded while still saturating the engine.
+  max_running_requests: 32
+  skip_tokenizer_init: true
diff --git a/examples/math/qwen3.5-4b-m2po-full/README.md b/examples/math/qwen3.5-4b-m2po-full/README.md
new file mode 100644
index 0000000..e37bebd
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/README.md
@@ -0,0 +1,97 @@
+# Qwen3.5-4B — Math RL (M2PO)
+
+Text-only math RL on **Qwen/Qwen3.5-4B** with **M2PO**, context 8k, lr 5e-6,
+DeepScaleR data, `math_verify` reward.
+
+Qwen3.5-4B is a **hybrid Gated-DeltaNet + attention multimodal** checkpoint
+(architecture `Qwen3_5ForConditionalGeneration`, `model_type: qwen3_5`); these
+recipes train it **text-only**. The checkpoint ships as an image-text-to-text
+model, so AstraFlow loads it via the `AutoModelForImageTextToText` path (the
+`model_type` is registered in `VALID_VISION_MODELS`), and the trainer uses
+`attn_impl: sdpa` because a prebuilt flash-attn is not ABI-compatible with this
+torch build.
+
+Two variants:
+
+| recipe | weight transfer |
+|---|---|
+| `qwen3.5-4b-m2po-full`  | full (push the whole model each sync) |
+| `qwen3.5-4b-m2po-delta` | delta (push only changed weights) |
+
+## Backend support
+
+These recipes run on the **FSDP** trainer backend only. **The Megatron backend
+does not support Qwen3.5 yet:** the HF→Megatron converter (`mbridge`) has no
+`qwen3_5` bridge and Megatron-Core has no Gated-DeltaNet layer spec, so the
+GDN-hybrid model cannot be built or weight-loaded under Megatron (TP/PP/EP).
+Supporting it would require a new `mbridge` model plus a Megatron GDN layer spec
+and weight mapping. Dense Qwen3 (standard attention) *does* run on Megatron — see
+`examples/math/qwen3-8b-megatron-delta`.
+
+## Validated environment
+
+These recipes were validated end-to-end on the following stack (8× L40 /
+Ada). The model and GDN kernels come from pip dependencies — there is no
+hand-patched framework source:
+
+| package | version |
+|---|---|
+| `torch` | `2.11.0+cu130` |
+| `transformers` | `5.8.1` |
+| `kernels` | `0.14.1` |
+| `sglang` | `0.5.13.post1` (published release with `qwen3_5` support), served with `attention_backend: flashinfer` |
+| `flash-linear-attention` (`fla`) | `0.5.0` |
+| `flashinfer-python` | `0.6.12` (pulled by sglang) |
+| attention impl | `sdpa` (set in `experiment.yaml`) |
+
+> **Hopper (H100) note.** On sm_90 the GDN backward must use `fla`'s tilelang
+> kernel (`FLA_TILELANG=1`) — `fla` blocks its triton path on Hopper as
+> numerically wrong (fla#640) — and the tilelang JIT needs a full CUDA toolkit
+> (`CUDA_HOME` with nvcc + CCCL headers; the pip-shipped nvcc has none). The
+> trainer launch script (`scripts/3_trainer_model0.sh`) now detects Hopper and
+> sets both automatically (respecting pre-set values). Re-validated end-to-end
+> on 8×H100: baseline overall avg@k 47.9 (matches the L40 47.8), step 50 → 57.8.
+
+> **Install note.** `pyproject.toml` pins the full validated stack:
+> `transformers==5.8.1` (with `kernels>=0.14,<0.15`), `torch==2.11.0`, and
+> `sglang==0.5.13.post1` — the published release that ships `qwen3_5` support (the
+> older `0.5.12.post1` predated it). It pulls `flashinfer 0.6.12` in automatically,
+> so `uv pip install -e ".[sglang]"` resolves the validated environment directly.
+
+## GPU layout (default, 8 GPUs)
+
+```text
+SERVICE_CUDA_VISIBLE_DEVICES=0,1,2,3  ->  RaaS / SGLang inference (model0, dp=4)
+TRAINER_MODEL0_GPUS=4,5,6,7           ->  Trainer model0 (FSDP, 4 GPUs)
+```
+
+Override those env vars to use different GPUs.
+
+## Run
+
+```bash
+bash examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh
+# delta variant:
+bash examples/math/qwen3.5-4b-m2po-delta/scripts/run_qwen3.5-4b-m2po-delta.sh
+```
+
+The launcher starts three processes (AstraFlow HTTP service, RaaS/SGLang
+inference, FSDP trainer). See `scripts/` for the per-process scripts and
+`yaml/` for the experiment / RaaS configs.
+
+## Validation
+
+Trained end-to-end on the stack above; eval rises steadily over training.
+Qwen3.5-4B-full, overall metrics across the eval suite:
+
+| metric | step 0 | step 80 | Δ |
+|---|---|---|---|
+| overall avg@k | 47.8% | 57.4% | +9.6 |
+| overall pass@k | 56.5% | 67.4% | +10.9 |
+
+The table above was produced on the predecessor SGLang git build. Both variants
+(`full` and `delta`) were subsequently re-validated end-to-end on the pinned
+`sglang==0.5.13.post1` release: training completes with no crashes, full
+(`shard_copy`) and delta (~7× compressed) weight transfer both function, and
+eval holds at the baseline (overall avg@k ≈ 49–51% over a short run) — i.e. the
+published-release pin introduces no regression versus the git build.
diff --git a/examples/math/qwen3.5-4b-m2po-full/scripts/1_astraflow.sh b/examples/math/qwen3.5-4b-m2po-full/scripts/1_astraflow.sh
new file mode 100755
index 0000000..4fe0968
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/scripts/1_astraflow.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+set -euo pipefail
+# [1/3] Launch AstraFlow HTTP service
+#
+# Usage (terminal 1):
+#   bash examples/math/qwen3.5-4b-m2po-full/scripts/1_astraflow.sh
+
+export CUDA_VISIBLE_DEVICES=""
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export ASTRAFLOW_HOST="${ASTRAFLOW_HOST:-0.0.0.0}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+echo "=== AstraFlow HTTP Service ==="
+echo "Experiment config : ${EXPERIMENT_CONFIG}"
+echo "Port              : ${ASTRAFLOW_PORT}"
+echo "==============================="
+
+python3 -u -m astraflow \
+  --config "${EXPERIMENT_CONFIG}" \
+  --port "${ASTRAFLOW_PORT}" \
+  --host "${ASTRAFLOW_HOST}" \
+  2>&1 | tee "${LOG_DIR}/astraflow.log"
diff --git a/examples/math/qwen3.5-4b-m2po-full/scripts/2_raas.sh b/examples/math/qwen3.5-4b-m2po-full/scripts/2_raas.sh
new file mode 100755
index 0000000..be71a71
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/scripts/2_raas.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+set -euo pipefail
+# [2/3] Launch RaaS inference server (SGLang + TCP receiver)
+#
+# Usage (terminal 2, after AstraFlow is ready):
+#   bash examples/math/qwen3.5-4b-m2po-full/scripts/2_raas.sh
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+export RAAS_CONFIG="${RAAS_CONFIG:-${YAML_DIR}/raas.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES:-0,1,2,3}"
+export RAAS_HOST="${RAAS_HOST:-0.0.0.0}"
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="${ASTRAFLOW_URL:-http://127.0.0.1:${ASTRAFLOW_PORT}}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+echo "=== RaaS Inference Server (SGLang + TCP receiver) ==="
+echo "Experiment config : ${EXPERIMENT_CONFIG}"
+echo "RaaS config       : ${RAAS_CONFIG}"
+echo "GPUs              : ${CUDA_VISIBLE_DEVICES}"
+echo "Port              : ${RAAS_PORT}"
+echo "AstraFlow URL     : ${ASTRAFLOW_URL}"
+echo "======================================================="
+
+python3 -u -m astraflow.raas.server \
+  --host "${RAAS_HOST}" \
+  --port "${RAAS_PORT}" \
+  --config "${EXPERIMENT_CONFIG}" \
+  --config "${RAAS_CONFIG}" \
+  --engine-id "${ENGINE_ID:-default}" \
+  --astraflow-url "${ASTRAFLOW_URL}" \
+  2>&1 | tee "${LOG_DIR}/raas.log"
diff --git a/examples/math/qwen3.5-4b-m2po-full/scripts/3_trainer_model0.sh b/examples/math/qwen3.5-4b-m2po-full/scripts/3_trainer_model0.sh
new file mode 100755
index 0000000..f941769
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/scripts/3_trainer_model0.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+set -euo pipefail
+# [3/3] Launch Trainer for model0 (TCP, sender_agent on local_rank 0)
+#
+# Usage (terminal 3, after AstraFlow and RaaS are ready):
+#   bash examples/math/qwen3.5-4b-m2po-full/scripts/3_trainer_model0.sh
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+astraflow_load_experiment_env
+
+export CUDA_VISIBLE_DEVICES="${TRAINER_MODEL0_GPUS:-4,5,6,7}"
+TRAINER0_NPROC="$(echo "${CUDA_VISIBLE_DEVICES}" | awk -F',' '{print NF}')"
+
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="http://127.0.0.1:${ASTRAFLOW_PORT}"
+export ASTRAFLOW_RAAS_URL="http://127.0.0.1:${RAAS_PORT}"
+
+# sender_agent (in trainer) listens on this HTTP port
+export WEIGHT_TRANSFER_HTTP_PORT="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0:-19861}"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR. Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+# Hopper needs FLA_TILELANG + CUDA_HOME for the GDN backward (see utils.sh).
+astraflow_setup_qwen35_hopper_env
+
+echo "=== Trainer model0 (TCP) ==="
+echo "Experiment config   : ${EXPERIMENT_CONFIG}"
+echo "GPUs                : ${CUDA_VISIBLE_DEVICES} (FSDP dp${TRAINER0_NPROC})"
+echo "AstraFlow           : ${ASTRAFLOW_URL}"
+echo "RaaS                : ${ASTRAFLOW_RAAS_URL}"
+echo "Sender HTTP         : ${WEIGHT_TRANSFER_HTTP_PORT}"
+echo "WANDB mode          : ${WANDB_MODE:-online}"
+echo "=========================================="
+
+torchrun --nnodes 1 --nproc-per-node "${TRAINER0_NPROC}" \
+  --master-addr "${MASTER_ADDR:-127.0.0.1}" --master-port "${MASTER_PORT_MODEL0:-29541}" \
+  examples/launch_trainer.py \
+  --config "${EXPERIMENT_CONFIG}" \
+  --trainer trainer_model0 \
+  "$@" 2>&1 | tee "${LOG_DIR}/trainer_model0.log"
diff --git a/examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh b/examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh
new file mode 100755
index 0000000..d548573
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh
@@ -0,0 +1,110 @@
+#!/bin/bash
+set -euo pipefail
+# All-in-one launcher for AstraFlow v2 math training (Qwen3.5-4B, M2PO, TCP).
+#
+# Launches 3 processes:
+#   1. AstraFlow HTTP service (CPU-only)
+#   2. RaaS inference server  (SGLang, SERVICE_CUDA_VISIBLE_DEVICES)
+#   3. Trainer model0         (math, TRAINER_MODEL0_GPUS)
+#
+# Requires: transformers>=5.8 (+ flash-linear-attention for training),
+#           SGLang main (qwen3_5 model). See yaml/raas.yaml for the backend note.
+#
+# Usage:
+#   bash examples/math/qwen3.5-4b-m2po-full/scripts/run_qwen3.5-4b-m2po-full.sh
+
+# =============================================================================
+# Part 1: Load env and settings
+# =============================================================================
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../../.." && pwd)"
+cd "${REPO_ROOT}"
+export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
+
+YAML_DIR="${SCRIPT_DIR}/yaml"
+export EXPERIMENT_CONFIG="${EXPERIMENT_CONFIG:-${YAML_DIR}/experiment.yaml}"
+export RAAS_CONFIG="${RAAS_CONFIG:-${YAML_DIR}/raas.yaml}"
+source "${REPO_ROOT}/examples/_common/utils.sh"
+# Export EXP_NAME and TRIAL_NAME from the experiment YAML.
+# Defined in examples/_common/utils.sh.
+astraflow_load_experiment_env
+
+# =============================================================================
+# Part 2: Set up env
+# =============================================================================
+# GPU assignments (default: 4 GPUs for inference, 4 for training)
+export SERVICE_CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES:-0,1,2,3}"
+export TRAINER_MODEL0_GPUS="${TRAINER_MODEL0_GPUS:-4,5,6,7}"
+# Ports / URLs (each component gets its own port)
+export RAAS_HOST="${RAAS_HOST:-0.0.0.0}"
+export RAAS_PORT="${RAAS_PORT:-19190}"
+export ASTRAFLOW_HOST="${ASTRAFLOW_HOST:-0.0.0.0}"
+export ASTRAFLOW_PORT="${ASTRAFLOW_PORT:-8000}"
+export ASTRAFLOW_URL="http://127.0.0.1:${ASTRAFLOW_PORT}"
+export WEIGHT_TRANSFER_HTTP_PORT_MODEL0="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0:-19861}"
+
+TRAINER0_NPROC="$(echo "${TRAINER_MODEL0_GPUS}" | awk -F',' '{print NF}')"
+
+# NCCL / PYTORCH / WANDB tweaks + LOG_DIR.
+# Defined in examples/_common/utils.sh.
+astraflow_setup_env
+
+# =============================================================================
+# Part 3: Print info and clean up
+# =============================================================================
+echo "=== AstraFlow (Qwen3.5-4B, math, M2PO, ctx8k, TCP full) ==="
+echo "Experiment config   : ${EXPERIMENT_CONFIG}"
+echo "RaaS config         : ${RAAS_CONFIG}"
+echo "RaaS GPUs           : ${SERVICE_CUDA_VISIBLE_DEVICES}"
+echo "Trainer model0 GPUs : ${TRAINER_MODEL0_GPUS} (FSDP dp${TRAINER0_NPROC})"
+echo "RaaS port           : ${RAAS_PORT}"
+echo "AstraFlow port      : ${ASTRAFLOW_PORT}"
+echo "Sender HTTP model0  : ${WEIGHT_TRANSFER_HTTP_PORT_MODEL0}"
+echo "WANDB mode          : ${WANDB_MODE:-online}"
+echo "=========================================================="
+
+trap astraflow_cleanup_trap EXIT INT TERM
+
+# Kill leftover processes and shared memory from prior runs.
+# Defined in examples/_common/utils.sh.
+astraflow_kill_stale
+
+# =============================================================================
+# Part 4: Launch training
+# =============================================================================
+echo "[1/3] Starting AstraFlow HTTP service..."
+CUDA_VISIBLE_DEVICES="" \
+  python3 -u -m astraflow \
+    --config "${EXPERIMENT_CONFIG}" \
+    --port "${ASTRAFLOW_PORT}" \
+    --host "${ASTRAFLOW_HOST}" \
+    2>&1 | tee "${LOG_DIR}/astraflow.log" &
+sleep 5
+
+echo "[2/3] Starting RaaS inference server (SGLang + TCP receiver)..."
+CUDA_VISIBLE_DEVICES="${SERVICE_CUDA_VISIBLE_DEVICES}" \
+  python3 -u -m astraflow.raas.server \
+    --host "${RAAS_HOST}" \
+    --port "${RAAS_PORT}" \
+    --config "${EXPERIMENT_CONFIG}" \
+    --config "${RAAS_CONFIG}" \
+    --engine-id "${ENGINE_ID:-default}" \
+    --astraflow-url "${ASTRAFLOW_URL}" \
+    2>&1 | tee "${LOG_DIR}/raas.log" &
+sleep 15
+
+export ASTRAFLOW_RAAS_URL="http://127.0.0.1:${RAAS_PORT}"
+
+# Hopper needs FLA_TILELANG + CUDA_HOME for the GDN backward (see utils.sh).
+astraflow_setup_qwen35_hopper_env
+
+echo "[3/3] Starting trainer model0..."
+CUDA_VISIBLE_DEVICES="${TRAINER_MODEL0_GPUS}" \
+WEIGHT_TRANSFER_HTTP_PORT="${WEIGHT_TRANSFER_HTTP_PORT_MODEL0}" \
+  torchrun --nnodes 1 --nproc-per-node "${TRAINER0_NPROC}" \
+    --master-addr "${MASTER_ADDR:-127.0.0.1}" --master-port "${MASTER_PORT_MODEL0:-29541}" \
+    examples/launch_trainer.py \
+    --config "${EXPERIMENT_CONFIG}" \
+    --trainer trainer_model0 \
+    "$@" \
+    2>&1 | tee "${LOG_DIR}/trainer_model0.log"
diff --git a/examples/math/qwen3.5-4b-m2po-full/yaml/experiment.yaml b/examples/math/qwen3.5-4b-m2po-full/yaml/experiment.yaml
new file mode 100644
index 0000000..739613a
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/yaml/experiment.yaml
@@ -0,0 +1,170 @@
+# ============================================================================
+# Experiment config — AstraFlow service + Trainer
+# Experiment: math / qwen3.5-4b-m2po-full
+#
+# Qwen3.5-4B math RL with M2PO, ctx 8k, lr 5e-6, full TCP weight transfer.
+#
+# NOTE: Qwen3.5-4B is a HYBRID (Gated-DeltaNet + attention) multimodal model
+#   (architecture Qwen3_5ForConditionalGeneration, model_type qwen3_5), trained
+#   here TEXT-ONLY for math RL. Requires transformers>=5.8 (+ `fla` kernels for
+#   training) and SGLang main (qwen3_5 + TritonGDNKernel) for inference.
+#   attn_impl=sdpa (prebuilt flash-attn is not ABI-compatible with this torch).
+#
+# GPU layout (default, 8 GPUs):
+#   SERVICE_CUDA_VISIBLE_DEVICES=0,1,2,3 -> RaaS (model0 dp=4)
+#   TRAINER_MODEL0_GPUS=4,5,6,7          -> Trainer model0 (FSDP, 4 GPUs)
+# ============================================================================
+
+# ── Experiment: identity, model, shared settings ──
+experiment:
+  experiment_name: astraflow-math
+  trial_name: qwen3.5-4b-m2po-full
+  fileroot: ./data-experiments/${experiment.experiment_name}/${experiment.trial_name}
+
+  model_path: "Qwen/Qwen3.5-4B"
+  tokenizer_path: "Qwen/Qwen3.5-4B"
+  seed: 1
+  dtype: bfloat16
+  weight_transfer_mode: tcp
+  weight_transfer_strategies: full
+
+# ── RaaS: what to generate (inference-level config) ──
+# model keys here also determine expected_model_ids for AstraFlow service
+raas:
+  models:
+    model0:
+      backend: sglang
+      gconfig:
+        n_samples: 8
+        temperature: 1.0
+        max_new_tokens: 4000
+        min_new_tokens: 0
+
+# ── AstraFlow: data pipeline ──
+# auto-derives: expected_model_ids from raas.models keys
+# auto-derives: dump_dir from experiment.fileroot
+dataflow:
+  host: "0.0.0.0"
+  port: 8000
+
+  buffer:
+    size: 10000
+    replay_size: 10000
+    replay_ratio: 0
+    max_staleness: 8
+    filter_function: filter_zero_adv
+
+  rollout_dataset:
+    dataset_fn: "astraflow.dataflow.dataset.deepscaler:get_deepscaler_rl_dataset"
+    max_length: 2000
+
+  workflow_spec:
+    workflow_cls: "rlvr"
+    reward_fn: "math_verify"
+    enable_thinking: false
+
+  eval_workflows:
+    math_eval:
+      workflow_cls: "rlvr"
+      reward_fn: "math_verify"
+      enable_thinking: false
+      gconfig_overrides:
+        temperature: 0.6
+        n_samples: 1
+
+  eval_datasets:
+    aime24:
+      dataset_fn: "astraflow.dataflow.dataset.aime24x4:get_aime_2024x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    aime25:
+      dataset_fn: "astraflow.dataflow.dataset.aime25x4:get_aime_2025x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    amc:
+      dataset_fn: "astraflow.dataflow.dataset.amc24:get_amc_2024x4_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    minerva:
+      dataset_fn: "astraflow.dataflow.dataset.minervamath:get_minerva_math_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+    math500:
+      dataset_fn: "astraflow.dataflow.dataset.math500:get_math500_test_dataset"
+      max_length: 2000
+      repeat: 4
+      eval_workflow: math_eval
+
+# ── Trainer base: shared config ──
+# auto-derives from experiment: experiment_name, trial_name, fileroot,
+#   tokenizer_path, seed, dtype, weight_transfer_mode
+# auto-derives from raas.models.<model_id>: actor.path, actor.max_new_tokens,
+#   ref.path
+# auto-derives: saver, recover, stats_logger fields from experiment section
+# auto-derives: cluster.name_resolve from experiment.fileroot
+# auto-derives: trial_name suffix from model_id (e.g. trial_name-model0)
+trainer_base:
+  total_train_steps: 800
+  train_batch_size: 256
+  n_samples: 8
+  engine:
+    backend: fsdp
+    data_parallel_size: 4
+
+  actor:
+    # Prebuilt flash-attn isn't ABI-compatible with this torch/cu130 build, so
+    # use sdpa: Qwen3.5's GDN linear-attn uses fla kernels and full-attn blocks
+    # use sdpa.
+    attn_impl: sdpa
+    gradient_checkpointing: true
+    mb_spec:
+      max_tokens_per_mb: 8192
+    optimizer:
+      type: adam
+      lr: 5e-6
+      weight_decay: 0.01
+      beta1: 0.9
+      beta2: 0.999
+      eps: 1e-8
+      lr_scheduler_type: constant
+      gradient_clipping: 1.0
+    # PPO / M2PO algorithm
+    m2_threshold: 0.01
+    eps_clip: 100.0
+    eps_clip_higher: 100.0
+    reward_scaling: 1
+    reward_bias: 0
+    kl_ctl: 0.00
+    kl_penalty_coef: 0.001
+    ppo_n_minibatches: 4
+    reward_norm: { mean_level: group, std_level: group }
+    adv_norm: { mean_level: batch, std_level: batch }
+
+  ref:
+    attn_impl: sdpa
+    mb_spec:
+      max_tokens_per_mb: 8192
+
+  recover:
+    mode: auto
+    freq_steps: 25
+
+  evaluator:
+    eval_at_start: false
+    freq_steps: 25
+
+  stats_logger:
+    wandb:
+      mode: online
+      id_suffix: "uid"
+
+# ── Trainer for model0 — only overrides ──
+trainer_model0:
+  model_id: model0
+  stats_logger:
+    wandb:
+      tags: ["m2po", "math", "astraflow-v2", "qwen3.5-4b", "tcp", "ctx8k", "hybrid-gdn"]
diff --git a/examples/math/qwen3.5-4b-m2po-full/yaml/raas.yaml b/examples/math/qwen3.5-4b-m2po-full/yaml/raas.yaml
new file mode 100644
index 0000000..29674d1
--- /dev/null
+++ b/examples/math/qwen3.5-4b-m2po-full/yaml/raas.yaml
@@ -0,0 +1,57 @@
+# ============================================================================
+# RaaS config — Inference serving instance (hardware/resources)
+# Experiment: math / qwen3.5-4b-m2po-full
+#
+# Hardware: 4x GPU, TP=1
+#   model0:  DP=4, TP=1
+#
+# Qwen3.5-4B is a hybrid Gated-DeltaNet model: SGLang allocates a Mamba/SSM
+# state cache (~10 GB) in addition to the KV cache, so keep mem_fraction_static
+# conservative. Served by SGLang main via its TritonGDNKernel backend.
+#
+# Merged with experiment.yaml at launch (--config experiment.yaml --config raas.yaml)
+# experiment.yaml provides: model_path, tokenizer_path, seed, dtype, models/gconfig
+# ============================================================================
+
+rollout:
+  max_concurrent_rollouts: 512
+  max_concurrent_evals: 64
+  pause_grace_period: 3
+  # Adaptive availability — drive /availability off sglang /get_load.
+  enable_adaptive_availability: true
+  target_waiting_queue_per_dp: 4
+  adaptive_step_size: 4
+  load_cache_ttl_ms: 100
+
+engine:
+  model0:
+    backend: sglang
+    data_parallel_size: 4
+
+sglang:
+  context_length: 8192
+  mem_fraction_static: 0.7
+  # [H100 fix] Chunk the prefill. With the default -1 (off), a batch's prefill
+  # upcasts the LM-head logits to fp32 in one shot -> worker stalls/OOMs ->
+  # rollout starvation -> "detokenizer unresponsive" health-check failures ->
+  # scheduler crash in forward_extend (observed on 8xH100). The qwen3-8b recipe
+  # already sets this; the GDN hybrid needs it too.
+  chunked_prefill_size: 8192
+  # Attention backend. NOTE: this is specific to Qwen3.5's HYBRID
+  # Gated-DeltaNet architecture, NOT a general L40 limitation:
+  #   - Plain dense Qwen3 (full-attention) runs fine with the default fa3 on
+  #     L40/Ada. But for Qwen3.5's GDN path, fa3 dispatches a Hopper-only
+  #     kernel (hopper/flash_fwd_launch_template.h) that fails on Ada (sm_89)
+  #     with "CUDA error: invalid argument" under real load.
+  #   - On non-Hopper archs SGLang auto-selects flashinfer (full-attn) +
+  #     triton (the GDN/linear-attn + mamba layers). Both flashinfer and
+  #     triton are verified working for Qwen3.5-4B here; flashinfer is set
+  #     explicitly (SGLang's literal auto-default on Ada/L40). triton is an
+  #     equally-valid alternative (set attention_backend: triton).
+  attention_backend: flashinfer
+  # Cap concurrency: with n_samples=8 x max_new_tokens=4000 and an
+  # unbounded queue, the hybrid GDN's KV + Mamba state cache overflows
+  # -> 'KV cache pool full, retract' -> CUDA OOM on a 44GB L40. 32 keeps
+  # peak KV bounded while still saturating the engine.
+  max_running_requests: 32
+  skip_tokenizer_init: true
diff --git a/pyproject.toml b/pyproject.toml
index 1f6b4b4..35e599f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,7 +47,7 @@ dependencies = [
     "torchdata",
     "huggingface_hub",
     "datasets>=3.0.0",
-    "transformers==5.6.1",
+    "transformers==5.8.1",
     "megatron-core==0.13.1",
     "mbridge==0.13.0",
     "torch_memory_saver==0.0.9.post1",
@@ -86,7 +86,7 @@ dependencies = [
     "numba",
     "blosc",
     "pybind11>=2.10.0",
-    "networkx==3.3",
+    "networkx==3.6.1",
     "aiofiles",
     "aiohttp>=3.11.10",
     "httpx>=0.28.1",
@@ -140,7 +140,17 @@ te = [
 ]
 
 sglang = [
-    "sglang==0.5.12.post1",
+    # Pinned to the validated published release 0.5.13.post1: it ships qwen3_5
+    # support (sglang/srt/models/qwen3_5.py) + the TritonGDNKernel and requires
+    # transformers==5.8.1. (A git build was pinned earlier while the then-latest
+    # 0.5.12.post1 still predated qwen3_5; 0.5.13.post1 supersedes it and is the
+    # release the Qwen3.5 + Qwen3-dense math recipes are validated against -- see
+    # examples/math/qwen3.5-4b-m2po-full/README.md.)
+    "sglang==0.5.13.post1",
+    # Fast Gated-DeltaNet kernels for Qwen3.5 GDN training. Optional (transformers
+    # falls back to a slower pure-torch path when absent), but the validated runs
+    # used it; pin to the validated version.
+    "flash-linear-attention==0.5.0",
 ]
 
 vllm = [
@@ -192,23 +202,21 @@ include = ["astraflow*"]
 exclude = ["tests*", "docs*", "examples*"]
 
 [tool.uv]
-# sglang 0.5.12 depends on flash-attn-4>=4.0.0b9 (a pre-release wheel, pulled
-# in automatically as a sglang dependency). Without this, `uv pip install
-# -e ".[sglang]"` fails to resolve with "pre-releases weren't enabled".
+# The pinned sglang release (0.5.13.post1) depends on flash-attn-4>=4.0.0b9 (a
+# pre-release wheel -- 4.0.0b15 in the validated env -- pulled in automatically as
+# a sglang dependency). Without this, `uv pip install -e ".[sglang]"` fails to
+# resolve with "pre-releases weren't enabled".
 prerelease = "allow"
 exclude-dependencies = ["flash-attn"]
 override-dependencies=[
     "outlines-core==0.1.26",
-    # sglang 0.5.12 pins transformers==5.6.0, which has a flash-attention bug
-    # (unconditional s_aux.to() crashes non-sink models like Qwen3). 5.6.1 is
-    # a patch release that fixes it; override sglang's exact pin to pick it up.
-    "transformers==5.6.1",
-    # sglang requires an unbounded "kernels", so uv resolves the latest (0.15+),
-    # but transformers 5.6.1 only supports kernels<0.13 (its hub_kernels module
-    # calls LayerRepository() without a revision/version, which 0.15 rejects ->
-    # `import sglang` crashes with "Either a revision or a version must be
-    # specified."). Pin to the range transformers 5.6.1 was built against.
-    "kernels>=0.12.0,<0.13",
+    # Pin transformers to 5.8.1, the version the pinned sglang release requires and
+    # the Qwen3.5 + Qwen3-dense math recipes are validated on
+    # (see examples/math/qwen3.5-4b-m2po-full/README.md).
+    "transformers==5.8.1",
+    # The pinned sglang release requires kernels<0.15; pin >=0.14 so uv selects the
+    # validated 0.14.x rather than an older kernels release.
+    "kernels>=0.14.0,<0.15",
 ]
 
 [tool.uv.extra-build-dependencies]