diff --git a/cookbooks/daytona-rl/.gitignore b/cookbooks/daytona-rl/.gitignore
new file mode 100644
index 000000000..b1847296e
--- /dev/null
+++ b/cookbooks/daytona-rl/.gitignore
@@ -0,0 +1 @@
+/train_run.json
diff --git a/cookbooks/daytona-rl/Dockerfile.hud b/cookbooks/daytona-rl/Dockerfile.hud
new file mode 100644
index 000000000..6a1eeb88d
--- /dev/null
+++ b/cookbooks/daytona-rl/Dockerfile.hud
@@ -0,0 +1,14 @@
+FROM python:3.11-slim
+
+RUN apt-get update && apt-get install -y --no-install-recommends curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+# Not uv.lock* — Docker lets that glob match nothing, Daytona's builder errors
+# with "Path does not exist: uv.lock*". The lock is committed, so copy it by name.
+COPY pyproject.toml uv.lock ./
+RUN pip install uv && uv sync --frozen --no-dev
+COPY env.py tasks.py bugs.py ./
+
+EXPOSE 8765
+CMD ["uv", "run", "hud", "serve", "env:env", "--host", "0.0.0.0", "--port", "8765"]
diff --git a/cookbooks/daytona-rl/README.md b/cookbooks/daytona-rl/README.md
new file mode 100644
index 000000000..08863c215
--- /dev/null
+++ b/cookbooks/daytona-rl/README.md
@@ -0,0 +1,79 @@
+# RL on Daytona sandboxes
+
+Run an agent evaluation where every rollout gets its own fresh
+[Daytona](https://daytona.io) sandbox — then train a model on the graded
+rollouts that same evaluation produced. One `env.py`, no second pipeline.
+
+The task: a small Python file with seeded bugs, graded by whether `pytest`
+passes. Ten GRPO steps at 128 parallel rollouts took a Qwen3.5 4B fork from
+**35.9% to 81.2%** pass rate on held-out bug variants it never trained on.
+
+![training curve](bench/train_curve.svg)
+
+The full measured walkthrough — spin-up ladders to 256 concurrent, warm pools,
+and sizing rules — is [the guide on Daytona's docs](DAYTONA_GUIDE_URL). The
+condensed HUD-side walkthrough is
+[on the HUD docs](https://docs.hud.ai/v6/cookbooks/daytona-rl).
+
+| File | Purpose |
+|------|---------|
+| `env.py` | The environment: a workspace, one task, a pytest grader |
+| `bugs.py` | 24 deterministic bug variants (4/5/6 broken functions) |
+| `tasks.py` | Task list for `hud eval` |
+| `train.py` | The published 10-step GRPO run |
+| `pool.py` | Warm-pool helpers (create, wait until really full, drop) |
+| `snapshot.py` | Content-addressed snapshot naming |
+| `bench/` | Repro receipts behind the guide's numbers |
+
+## Setup
+
+```bash
+uv sync
+hud set HUD_API_KEY=...        # https://hud.ai/project/api-keys
+export DAYTONA_API_KEY=...     # https://app.daytona.io
+```
+
+`DAYTONA_API_KEY` has to be a real `export`: the Daytona SDK reads the process
+environment, not `~/.hud/.env`.
+
+## Run
+
+```bash
+hud eval tasks.py claude                                   # one local rollout, no Daytona
+uv run train.py --steps 10 --group 8 --concurrent 128      # the published training run
+PYTHONPATH=. uv run bench/reap.py --delete                 # clean up stray sandboxes
+```
+
+`train.py` builds the snapshot from `Dockerfile.hud` on first run, keeps a warm
+pool the width of the batch, and appends per-step metrics to `train_run.json`.
+Variants 0-15 train; 16-23 are held out for the before/after measurement.
+
+The snapshot name is a hash of `env.py`, `bugs.py`, `Dockerfile.hud` and
+`pyproject.toml` (`snapshot.py`), so editing the environment mints a new one
+instead of silently running the old image.
+
+## `bench/`
+
+Run from this directory with `PYTHONPATH=.`.
+
+| File | Purpose |
+|------|---------|
+| `baseline.py` | Pass rate with no optimizer — the 35.9% before and 81.2% after |
+| `run_daytona.py` | One rollout on Daytona, with the spin-up/agent time split |
+| `ladder.py`, `ladder_reps.csv` | Concurrency ladder, N up to 256 |
+| `warmpool.py`, `warmpool_repro.py` | Warm-pool A/B |
+| `train_run.json`, `train_curve.svg` | The published training curve |
+| `reap.py` | Delete stray sandboxes and stale snapshots |
+
+## Two things that will bite you
+
+**A warm pool says it's full before it is.** Its `current_size` reports the target
+about 12 seconds early. `pool.py` counts actual sandboxes instead — and that
+count needs `include_warm=True`, because unclaimed pool members are excluded from
+`list_sandboxes` by default. Count the obvious way and you get 0 forever.
+
+**An interrupted run can strand a pool.** `train.py` drops its pool on exit and
+on SIGINT/SIGTERM, but a hard kill leaves one parked. `bench/reap.py` will not
+catch it — it calls `AsyncDaytona.list()`, blind to unclaimed pool sandboxes for
+the same `include_warm` reason, and it does not delete pools at all. Check after
+any interrupted run.
diff --git a/cookbooks/daytona-rl/bench/baseline.py b/cookbooks/daytona-rl/bench/baseline.py
new file mode 100644
index 000000000..4e16f4bde
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/baseline.py
@@ -0,0 +1,106 @@
+"""Probe the task's difficulty before committing to a training run.
+
+    uv run baseline.py                       # 24 variants, 1 rollout each
+    uv run baseline.py --variants 24 --group 2
+
+Runs the same agent config training would use, with no optimizer. The decision
+this informs: a ~20-40% pass rate leaves room for GRPO to learn. Near 0% or near
+100% means retune the task instead of spending hours discovering the gradient was
+dead.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import time
+from collections import Counter
+
+from daytona import Image
+from hud.agents import create_agent
+from hud.eval import DaytonaRuntime, Job, Taskset
+
+import bugs
+from env import fix_calc
+from snapshot import snapshot_name
+
+MODEL = "daytona-calc-3"
+
+
+async def main(*, variants: int, group: int, max_concurrent: int, start: int, model: str) -> None:
+    agent = create_agent(
+        model,
+        completion_kwargs={"max_tokens": 2048, "extra_body": {"return_token_ids": True}},
+    )
+    variant_ids = list(range(start, start + variants))
+    taskset = Taskset("calc", [fix_calc(variant=v) for v in variant_ids])
+    runtime = DaytonaRuntime(snapshot_name(), image=Image.from_dockerfile("Dockerfile.hud"))
+
+    print(
+        f"model={model} variants={variant_ids} group={group} rollouts={len(variant_ids) * group} tests={bugs.test_count()}"
+    )
+    session = await Job.start(f"calc-baseline-{start}-{start + variants - 1}", group=group)
+    t0 = time.perf_counter()
+    await taskset.run(agent, runtime=runtime, job=session, max_concurrent=max_concurrent)
+    wall = time.perf_counter() - t0
+
+    runs = session.runs
+    rewards = [r.reward for r in runs]
+    solved = sum(1 for r in rewards if r == 1.0)
+
+    by_k: Counter = Counter()
+    solved_by_k: Counter = Counter()
+    unattributed = 0
+    for run in runs:
+        variant = getattr(run, "_args", {}).get("variant")
+        if variant is None:
+            unattributed += 1
+            continue
+        k = len(bugs.broken_for(variant))
+        by_k[k] += 1
+        if run.reward == 1.0:
+            solved_by_k[k] += 1
+
+    launched = len(runs) - unattributed
+    print(f"\nwall {wall:.1f}s | runs {len(runs)} | launched {launched}")
+
+    if unattributed:
+        print(
+            f"\n{unattributed}/{len(runs)} rollouts never launched — no difficulty "
+            f"signal here. Check the env starts in the sandbox (a missing file in "
+            f"Dockerfile.hud shows up as \"env closed connection during 'hello'\")."
+        )
+        return
+
+    print(f"pass rate: {solved}/{launched} = {solved / launched:.1%}")
+    for k in sorted(by_k):
+        print(f"  k={k} bugs: {solved_by_k[k]}/{by_k[k]} = {solved_by_k[k] / by_k[k]:.1%}")
+
+    rate = solved / launched
+    verdict = (
+        "in range — a training run is justified"
+        if 0.15 <= rate <= 0.55
+        else "too easy — add bugs or harder ones"
+        if rate > 0.55
+        else "too hard — reduce k or drop the subtlest bugs"
+    )
+    print(f"\nverdict: {rate:.1%} {verdict}")
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--variants", type=int, default=24)
+    p.add_argument("--group", type=int, default=1)
+    p.add_argument("--max-concurrent", type=int, default=12)
+    p.add_argument("--start", type=int, default=0)
+    p.add_argument("--model", default=MODEL)
+    a = p.parse_args()
+    asyncio.run(
+        main(
+            variants=a.variants,
+            group=a.group,
+            max_concurrent=a.max_concurrent,
+            start=a.start,
+            model=a.model,
+        )
+    )
diff --git a/cookbooks/daytona-rl/bench/ladder.py b/cookbooks/daytona-rl/bench/ladder.py
new file mode 100644
index 000000000..611375c72
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/ladder.py
@@ -0,0 +1,187 @@
+"""Concurrency ladder for Daytona sandboxes: N in {1, 8, 32, 128}.
+
+    uv run ladder.py                      # infra mode, no LLM spend
+    uv run ladder.py --mode rollout       # real Claude rollouts (costs tokens)
+    uv run ladder.py --levels 1,8         # override the ladder
+
+Infra mode measures the thing we actually doubt: how sandbox spin-up and the
+one-SSH-connection-per-sandbox transport (runtime.py:780) behave under fan-out.
+Each worker acquires a sandbox, opens a *fresh control connection* to it, does
+one real handshake (`client.manifest()`), and exits — no agent, no tokens.
+
+Writes ladder.csv and ladder.svg.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import csv
+import resource
+import statistics
+import time
+from pathlib import Path
+
+from daytona import Image
+from hud.clients import connect
+from hud.eval import DaytonaRuntime
+
+from env import fix_calc
+from snapshot import snapshot_name
+from timing import TimedProvider
+
+SNAPSHOT = snapshot_name()
+OUT_CSV = Path("ladder.csv")
+OUT_SVG = Path("ladder.svg")
+
+
+async def one_infra(provider, idx: int) -> dict:
+    """Acquire a sandbox, open a fresh control connection, handshake, tear down."""
+    t0 = time.perf_counter()
+    row = {"idx": idx, "spin_up_s": None, "connect_s": None, "ok": False, "error": ""}
+    try:
+        async with provider(fix_calc()) as rt:
+            row["spin_up_s"] = time.perf_counter() - t0
+            t1 = time.perf_counter()
+            async with connect(rt) as client:
+                assert client.manifest is not None
+                row["connect_s"] = time.perf_counter() - t1
+            row["ok"] = True
+    except Exception as exc:
+        row["error"] = f"{type(exc).__name__}: {exc}"
+    row["total_s"] = time.perf_counter() - t0
+    return row
+
+
+async def one_rollout(provider, idx: int) -> dict:
+    from hud.agents.claude import ClaudeAgent
+
+    timed = TimedProvider(provider)
+    t0 = time.perf_counter()
+    row = {"idx": idx, "spin_up_s": None, "connect_s": None, "ok": False, "error": ""}
+    try:
+        job = await fix_calc().run(ClaudeAgent(), runtime=timed)
+        row["ok"] = True
+        row["reward"] = job.reward
+        row["spin_up_s"] = timed.spin_ups[0] if timed.spin_ups else None
+    except Exception as exc:
+        row["error"] = f"{type(exc).__name__}: {exc}"
+    row["total_s"] = time.perf_counter() - t0
+    return row
+
+
+async def run_level(n: int, mode: str) -> dict:
+    provider = DaytonaRuntime(SNAPSHOT, image=Image.from_dockerfile("Dockerfile.hud"))
+    worker = one_infra if mode == "infra" else one_rollout
+
+    t0 = time.perf_counter()
+    rows = await asyncio.gather(*(worker(provider, i) for i in range(n)))
+    wall = time.perf_counter() - t0
+
+    ok = [r for r in rows if r["ok"]]
+    spin = sorted(r["spin_up_s"] for r in ok if r["spin_up_s"] is not None)
+    errors = [r["error"] for r in rows if r["error"]]
+
+    def pct(p: float) -> float | None:
+        if not spin:
+            return None
+        return spin[min(len(spin) - 1, int(p * len(spin)))]
+
+    summary = {
+        "n": n,
+        "mode": mode,
+        "wall_s": round(wall, 2),
+        "ok": len(ok),
+        "failed": n - len(ok),
+        "per_min": round(len(ok) / wall * 60, 1) if wall else 0.0,
+        "spin_min_s": round(spin[0], 2) if spin else None,
+        "spin_med_s": round(statistics.median(spin), 2) if spin else None,
+        "spin_p90_s": round(pct(0.9), 2) if spin else None,
+        "spin_max_s": round(spin[-1], 2) if spin else None,
+    }
+    print(f"[N={n:>3}] {summary}")
+    for e in dict.fromkeys(errors):
+        print(f"        error: {e[:200]}")
+    return summary
+
+
+def chart(rows: list[dict]) -> None:
+    """Minimal hand-rolled SVG: throughput bars + median spin-up line."""
+    w, h, pad = 640, 320, 56
+    plot_w, plot_h = w - 2 * pad, h - 2 * pad
+    max_rate = max((r["per_min"] or 0) for r in rows) or 1
+    max_spin = max((r["spin_max_s"] or 0) for r in rows) or 1
+    bar_w = plot_w / max(len(rows), 1) * 0.55
+
+    parts = [
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{h}" '
+        f'viewBox="0 0 {w} {h}" font-family="ui-sans-serif,system-ui,sans-serif">',
+        f'<rect width="{w}" height="{h}" fill="#fff"/>',
+        f'<text x="{pad}" y="28" font-size="14" font-weight="600">'
+        f"Daytona concurrency ladder — {rows[0]['mode']} mode</text>",
+        f'<line x1="{pad}" y1="{pad + plot_h}" x2="{pad + plot_w}" y2="{pad + plot_h}" '
+        f'stroke="#999"/>',
+    ]
+    pts = []
+    for i, r in enumerate(rows):
+        cx = pad + plot_w * (i + 0.5) / len(rows)
+        bh = plot_h * (r["per_min"] or 0) / max_rate
+        parts.append(
+            f'<rect x="{cx - bar_w / 2:.1f}" y="{pad + plot_h - bh:.1f}" '
+            f'width="{bar_w:.1f}" height="{bh:.1f}" fill="#6366f1" opacity="0.85"/>'
+        )
+        parts.append(
+            f'<text x="{cx:.1f}" y="{pad + plot_h + 16:.1f}" font-size="11" '
+            f'text-anchor="middle">N={r["n"]}</text>'
+        )
+        parts.append(
+            f'<text x="{cx:.1f}" y="{pad + plot_h - bh - 6:.1f}" font-size="10" '
+            f'text-anchor="middle" fill="#4338ca">{r["per_min"]}/min</text>'
+        )
+        sy = pad + plot_h - plot_h * (r["spin_med_s"] or 0) / max_spin
+        pts.append(f"{cx:.1f},{sy:.1f}")
+        parts.append(f'<circle cx="{cx:.1f}" cy="{sy:.1f}" r="3.5" fill="#ef4444"/>')
+        if r["failed"]:
+            parts.append(
+                f'<text x="{cx:.1f}" y="{pad - 8:.1f}" font-size="10" '
+                f'text-anchor="middle" fill="#dc2626">{r["failed"]} failed</text>'
+            )
+    parts.append(
+        f'<polyline points="{" ".join(pts)}" fill="none" stroke="#ef4444" stroke-width="2"/>'
+    )
+    parts.append(
+        f'<text x="{pad}" y="{h - 14}" font-size="11" fill="#4338ca">bars: completions/min</text>'
+        f'<text x="{pad + 190}" y="{h - 14}" font-size="11" fill="#ef4444">'
+        f"line: median spin-up (max {max_spin}s)</text>"
+    )
+    parts.append("</svg>")
+    OUT_SVG.write_text("\n".join(parts))
+
+
+async def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--mode", choices=("infra", "rollout"), default="infra")
+    ap.add_argument("--levels", default="1,8,32,128")
+    args = ap.parse_args()
+
+    levels = [int(x) for x in args.levels.split(",")]
+    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+    want = max(levels) * 16 + 256
+    if soft < want:
+        resource.setrlimit(resource.RLIMIT_NOFILE, (min(want, hard), hard))
+    print(f"fd limit: {soft} -> {resource.getrlimit(resource.RLIMIT_NOFILE)[0]} (hard {hard})")
+
+    rows = []
+    for n in levels:
+        rows.append(await run_level(n, args.mode))
+
+    with OUT_CSV.open("w", newline="") as fh:
+        writer = csv.DictWriter(fh, fieldnames=list(rows[0]))
+        writer.writeheader()
+        writer.writerows(rows)
+    chart(rows)
+    print(f"\nwrote {OUT_CSV} and {OUT_SVG}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/cookbooks/daytona-rl/bench/ladder_reps.csv b/cookbooks/daytona-rl/bench/ladder_reps.csv
new file mode 100644
index 000000000..f6489e5da
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/ladder_reps.csv
@@ -0,0 +1,13 @@
+n,rep,mode,ok,failed,ready_p50_s,ready_p95_s,ready_min_s,ready_max_s,spread_s,wall_s,errors
+8,1,cold,8,0,3.28,3.49,2.92,3.5,0.58,7.97,[]
+8,2,cold,8,0,2.78,3.19,2.63,3.32,0.69,7.47,[]
+8,3,cold,8,0,2.8,2.98,2.68,3.02,0.34,7.46,[]
+32,1,cold,32,0,3.16,3.49,2.86,3.55,0.68,7.97,[]
+32,2,cold,32,0,3.3,3.62,2.59,3.7,1.11,7.97,[]
+32,3,cold,32,0,3.27,4.69,2.94,4.76,1.82,14.51,[]
+128,1,cold,128,0,5.05,5.6,4.4,5.82,1.42,15.11,[]
+128,2,cold,128,0,5.17,8.99,4.49,9.51,5.02,16.17,[]
+128,3,cold,128,0,5.09,6.24,4.43,6.38,1.95,13.6,[]
+256,1,cold,256,0,9.85,10.34,8.77,11.34,2.58,42.47,[]
+256,2,cold,256,0,10.02,10.67,8.84,11.36,2.52,47.89,[]
+256,3,cold,256,0,9.9,11.12,8.37,15.11,6.75,62.11,[]
diff --git a/cookbooks/daytona-rl/bench/reap.py b/cookbooks/daytona-rl/bench/reap.py
new file mode 100644
index 000000000..4666301e4
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/reap.py
@@ -0,0 +1,81 @@
+"""Delete leftover Daytona sandboxes and stale snapshots.
+
+    uv run reap.py                        # list what would be deleted
+    uv run reap.py --delete               # delete stranded sandboxes
+    uv run reap.py --snapshots --delete   # also delete snapshots except the current hash
+
+Sandboxes: DaytonaRuntime sets auto_stop_interval=0 (never auto-stop) and relies
+on its own teardown, wrapped in contextlib.suppress. A killed client or a failed
+delete strands them, billing until someone notices.
+
+Snapshots: content-addressed names (see snapshot.py) mean every edit to env.py or
+Dockerfile.hud mints a new one, and nothing reaps the old.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+
+from daytona import AsyncDaytona
+
+from snapshot import snapshot_name
+
+PREFIX = "hud-smoke"
+
+
+async def reap_sandboxes(daytona: AsyncDaytona, *, delete: bool, prefix: str) -> None:
+    sandboxes = [s async for s in daytona.list()]
+    ours = [s for s in sandboxes if str(getattr(s, "snapshot", "") or "").startswith(prefix)]
+
+    print(f"{len(sandboxes)} sandboxes total, {len(ours)} matching {prefix!r}")
+    for s in ours:
+        print(f"  {s.id} state={s.state} snapshot={s.snapshot} created={s.created_at}")
+    if not ours or not delete:
+        if ours:
+            print("  dry run — pass --delete to remove these")
+        return
+
+    results = await asyncio.gather(*(daytona.delete(s) for s in ours), return_exceptions=True)
+    failed = [(s.id, r) for s, r in zip(ours, results) if isinstance(r, Exception)]
+    print(f"  deleted {len(ours) - len(failed)}/{len(ours)}")
+    for sid, err in failed:
+        print(f"    FAILED {sid}: {type(err).__name__}: {err}")
+
+
+async def reap_snapshots(daytona: AsyncDaytona, *, delete: bool, prefix: str) -> None:
+    keep = snapshot_name()
+    page = await daytona.snapshot.list()
+    ours = [s for s in page.items if s.name.startswith(prefix) and s.name != keep]
+
+    print(f"\n{len(ours)} stale snapshots matching {prefix!r} (keeping {keep})")
+    for s in ours:
+        print(f"  {s.name} {s.state} size={getattr(s, 'size', None)}")
+    if not ours or not delete:
+        if ours:
+            print("  dry run — pass --delete to remove these")
+        return
+
+    results = await asyncio.gather(
+        *(daytona.snapshot.delete(s) for s in ours), return_exceptions=True
+    )
+    failed = [(s.name, r) for s, r in zip(ours, results) if isinstance(r, Exception)]
+    print(f"  deleted {len(ours) - len(failed)}/{len(ours)}")
+    for name, err in failed:
+        print(f"    FAILED {name}: {type(err).__name__}: {err}")
+
+
+async def main(*, delete: bool, prefix: str, snapshots: bool) -> None:
+    async with AsyncDaytona() as daytona:
+        await reap_sandboxes(daytona, delete=delete, prefix=prefix)
+        if snapshots:
+            await reap_snapshots(daytona, delete=delete, prefix=prefix)
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--delete", action="store_true")
+    p.add_argument("--snapshots", action="store_true")
+    p.add_argument("--prefix", default=PREFIX)
+    a = p.parse_args()
+    asyncio.run(main(delete=a.delete, prefix=a.prefix, snapshots=a.snapshots))
diff --git a/cookbooks/daytona-rl/bench/run_daytona.py b/cookbooks/daytona-rl/bench/run_daytona.py
new file mode 100644
index 000000000..95c8e3b13
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/run_daytona.py
@@ -0,0 +1,46 @@
+"""One rollout of the smoke task on a Daytona sandbox, with timings.
+
+    uv run run_daytona.py
+
+Separates the two costs the guide needs to quote: sandbox spin-up (snapshot
+resolve + create + serve + ssh forward) and agent time.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import time
+
+from daytona import Image
+from hud.agents import create_agent
+from hud.eval import DaytonaRuntime
+
+from env import fix_calc
+from snapshot import snapshot_name
+from timing import TimedProvider
+
+SNAPSHOT = snapshot_name()
+
+# The model this repo trains. Any gateway model works — swap it for "claude" or
+# a base model to see the untrained behaviour.
+MODEL = "daytona-calc-3"
+
+
+async def main() -> None:
+    provider = TimedProvider(
+        DaytonaRuntime(SNAPSHOT, image=Image.from_dockerfile("Dockerfile.hud"))
+    )
+
+    t0 = time.perf_counter()
+    job = await fix_calc().run(create_agent(MODEL), runtime=provider)
+    total = time.perf_counter() - t0
+
+    spin_up = provider.spin_ups[0] if provider.spin_ups else float("nan")
+    print(f"\n[result]  reward={job.reward}")
+    print(f"[timing]  spin_up={spin_up:.1f}s  agent={total - spin_up:.1f}s  total={total:.1f}s")
+    if provider.failures:
+        print(f"[failures] {provider.failures}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/cookbooks/daytona-rl/bench/timing.py b/cookbooks/daytona-rl/bench/timing.py
new file mode 100644
index 000000000..b0123d28a
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/timing.py
@@ -0,0 +1,32 @@
+"""Wrap any HUD Provider to record how long acquiring its sandbox took.
+
+A Provider is just ``__call__(task) -> AsyncContextManager[Runtime]``
+(hud/eval/runtime.py:118), so timing one needs no SDK changes.
+"""
+
+from __future__ import annotations
+
+import time
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class TimedProvider:
+    """Delegates to *inner*, appending each spin-up duration to :attr:`spin_ups`."""
+
+    inner: Any
+    spin_ups: list[float] = field(default_factory=list)
+    failures: list[str] = field(default_factory=list)
+
+    @asynccontextmanager
+    async def __call__(self, task: Any):
+        t0 = time.perf_counter()
+        try:
+            async with self.inner(task) as rt:
+                self.spin_ups.append(time.perf_counter() - t0)
+                yield rt
+        except Exception as exc:
+            self.failures.append(f"{type(exc).__name__}: {exc}")
+            raise
diff --git a/cookbooks/daytona-rl/bench/train_curve.svg b/cookbooks/daytona-rl/bench/train_curve.svg
new file mode 100644
index 000000000..b8a396b0c
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/train_curve.svg
@@ -0,0 +1,41 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="720" height="400" viewBox="0 0 720 400" font-family="ui-sans-serif,system-ui,sans-serif">
+<rect width="720" height="400" fill="#ffffff"/>
+<text x="64" y="26" font-size="14" font-weight="600" fill="#111">Pass rate over 10 GRPO steps (128 rollouts per step)</text>
+<text x="64" y="43" font-size="11" fill="#666">Qwen3.5-4B on seeded pytest bugs; dashed lines are held-out variants 16-23</text>
+<line x1="64" y1="344.0" x2="590" y2="344.0" stroke="#eee"/>
+<text x="56" y="348.0" font-size="10" fill="#888" text-anchor="end">0%</text>
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+<text x="56" y="204.0" font-size="10" fill="#888" text-anchor="end">50%</text>
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+<text x="56" y="132.0" font-size="10" fill="#888" text-anchor="end">75%</text>
+<line x1="64" y1="56.0" x2="590" y2="56.0" stroke="#eee"/>
+<text x="56" y="60.0" font-size="10" fill="#888" text-anchor="end">100%</text>
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+<text x="598" y="114.1" font-size="10" fill="#047857">held-out after 81%</text>
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+<text x="239.3" y="362.0" font-size="10" text-anchor="middle" fill="#444">3</text>
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+<circle cx="473.1" cy="166.2" r="4" fill="#4338ca"/>
+<text x="473.1" y="362.0" font-size="10" text-anchor="middle" fill="#444">7</text>
+<circle cx="531.6" cy="134.7" r="4" fill="#4338ca"/>
+<text x="531.6" y="362.0" font-size="10" text-anchor="middle" fill="#444">8</text>
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+<text x="590.0" y="362.0" font-size="10" text-anchor="middle" fill="#444">9</text>
+<text x="327.0" y="382" font-size="11" fill="#555" text-anchor="middle">training step</text>
+</svg>
\ No newline at end of file
diff --git a/cookbooks/daytona-rl/bench/train_run.json b/cookbooks/daytona-rl/bench/train_run.json
new file mode 100644
index 000000000..6c4085ae9
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/train_run.json
@@ -0,0 +1,162 @@
+[
+ {
+  "step": 0,
+  "runs": 128,
+  "solved": 47,
+  "pass_rate": 0.3672,
+  "errored": 0,
+  "group_spread": 0.3818,
+  "samples": 1257,
+  "tokens": 222111,
+  "rollout_s": 117.3,
+  "loss": -5962.565,
+  "datums": 1097,
+  "chunks": 8,
+  "train_s": 106.8,
+  "optim_step": 1
+ },
+ {
+  "step": 1,
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+  "solved": 49,
+  "pass_rate": 0.3828,
+  "errored": 0,
+  "group_spread": 0.4015,
+  "samples": 1263,
+  "tokens": 207297,
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+  "loss": -7311.4616,
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+  "chunks": 8,
+  "train_s": 95.3,
+  "optim_step": 2
+ },
+ {
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+  "solved": 45,
+  "pass_rate": 0.3516,
+  "errored": 0,
+  "group_spread": 0.357,
+  "samples": 1264,
+  "tokens": 200866,
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+  "loss": -3977.6133,
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+  "chunks": 8,
+  "train_s": 86.9,
+  "optim_step": 3
+ },
+ {
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+  "optim_step": 4
+ },
+ {
+  "step": 4,
+  "runs": 128,
+  "solved": 60,
+  "pass_rate": 0.4688,
+  "errored": 0,
+  "group_spread": 0.3919,
+  "samples": 1260,
+  "tokens": 216219,
+  "rollout_s": 119.1,
+  "loss": -4851.6556,
+  "datums": 1180,
+  "chunks": 8,
+  "train_s": 92.9,
+  "optim_step": 5
+ },
+ {
+  "step": 5,
+  "runs": 128,
+  "solved": 71,
+  "pass_rate": 0.5547,
+  "errored": 0,
+  "group_spread": 0.3742,
+  "samples": 1232,
+  "tokens": 220862,
+  "rollout_s": 122.2,
+  "loss": -4223.9484,
+  "datums": 1076,
+  "chunks": 8,
+  "train_s": 90.9,
+  "optim_step": 6
+ },
+ {
+  "step": 6,
+  "runs": 128,
+  "solved": 81,
+  "pass_rate": 0.6328,
+  "errored": 0,
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+  "samples": 1240,
+  "tokens": 216717,
+  "rollout_s": 110.6,
+  "loss": -4265.8137,
+  "datums": 1240,
+  "chunks": 8,
+  "train_s": 96.5,
+  "optim_step": 7
+ },
+ {
+  "step": 7,
+  "runs": 128,
+  "solved": 79,
+  "pass_rate": 0.6172,
+  "errored": 0,
+  "group_spread": 0.4175,
+  "samples": 1213,
+  "tokens": 221413,
+  "rollout_s": 123.3,
+  "loss": -6306.1948,
+  "datums": 1135,
+  "chunks": 8,
+  "train_s": 97.3,
+  "optim_step": 8
+ },
+ {
+  "step": 8,
+  "runs": 128,
+  "solved": 93,
+  "pass_rate": 0.7266,
+  "errored": 0,
+  "group_spread": 0.3422,
+  "samples": 1189,
+  "tokens": 213918,
+  "rollout_s": 149.3,
+  "loss": -3653.8206,
+  "datums": 966,
+  "chunks": 8,
+  "train_s": 90.2,
+  "optim_step": 9
+ },
+ {
+  "step": 9,
+  "runs": 128,
+  "solved": 85,
+  "pass_rate": 0.6641,
+  "errored": 0,
+  "group_spread": 0.3321,
+  "samples": 1170,
+  "tokens": 218654,
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+  "datums": 894,
+  "chunks": 8,
+  "train_s": 84.5,
+  "optim_step": 10
+ }
+]
\ No newline at end of file
diff --git a/cookbooks/daytona-rl/bench/warmpool.py b/cookbooks/daytona-rl/bench/warmpool.py
new file mode 100644
index 000000000..df52722ed
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/warmpool.py
@@ -0,0 +1,136 @@
+"""A/B the Daytona warm-pool effect on sandbox spin-up.
+
+    uv run warmpool.py --n 8
+
+Warm pools are absent from the high-level `daytona` SDK; they live in the
+generated client (`WarmPoolsApi`). This measures one concurrent wave of N
+acquisitions with no pool, then creates a pool of N, waits for it to fill, and
+measures an identical wave.
+
+Open question the numbers answer: DaytonaRuntime creates sandboxes with
+ephemeral=True and auto_stop_interval=0, so a pooled sandbox is only reused if
+the pool matches those create parameters.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import os
+import statistics
+import time
+
+from daytona import Image
+from daytona_api_client_async import ApiClient, Configuration, WarmPoolsApi
+from daytona_api_client_async.models.create_warm_pool import CreateWarmPool
+from hud.clients import connect
+from hud.eval import DaytonaRuntime
+
+import pool
+from env import fix_calc
+from snapshot import snapshot_name
+
+HOST = "https://app.daytona.io/api"
+
+
+def _api() -> ApiClient:
+    return ApiClient(Configuration(host=HOST, access_token=os.environ["DAYTONA_API_KEY"]))
+
+
+async def one(provider) -> tuple[float | None, str]:
+    t0 = time.perf_counter()
+    try:
+        async with provider(fix_calc(variant=0)) as rt:
+            elapsed = time.perf_counter() - t0
+            async with connect(rt) as client:
+                assert client.manifest is not None
+            return elapsed, ""
+    except Exception as exc:
+        return None, f"{type(exc).__name__}: {exc}"
+
+
+async def wave(n: int, label: str) -> dict:
+    provider = DaytonaRuntime(snapshot_name(), image=Image.from_dockerfile("Dockerfile.hud"))
+    t0 = time.perf_counter()
+    results = await asyncio.gather(*(one(provider) for _ in range(n)))
+    wall = time.perf_counter() - t0
+
+    spins = sorted(s for s, _ in results if s is not None)
+    errors = [e for _, e in results if e]
+    row = {
+        "label": label,
+        "n": n,
+        "ok": len(spins),
+        "failed": n - len(spins),
+        "wall_s": round(wall, 1),
+        "p50": round(statistics.median(spins), 2) if spins else None,
+        "min": round(spins[0], 2) if spins else None,
+        "max": round(spins[-1], 2) if spins else None,
+    }
+    print(f"[{label}] {row}", flush=True)
+    for e in dict.fromkeys(errors):
+        print(f"    {e[:180]}", flush=True)
+    return row
+
+
+async def fill_pool(snapshot: str, n: int, target: str, timeout_s: int = 600) -> int:
+    """Create the pool, then wait for sandboxes that actually exist.
+
+    The pool's own ``current_size`` reports the target roughly 12s before the
+    sandboxes are there, so gating on it starts the warm wave against a
+    half-empty pool and understates the benefit. ``pool.wait_full`` counts real
+    unclaimed sandboxes instead.
+    """
+    async with _api() as api:
+        await WarmPoolsApi(api).create_warm_pool(
+            CreateWarmPool(snapshot=snapshot, pool=n, target=target)
+        )
+    return await pool.wait_full(snapshot, n, timeout_s=timeout_s)
+
+
+async def drop_pool(snapshot: str) -> None:
+    async with _api() as api:
+        pools = WarmPoolsApi(api)
+        for p in await pools.list_warm_pools():
+            if p.snapshot == snapshot:
+                await pools.delete_warm_pool(p.id)
+                print(f"deleted warm pool {p.id}")
+
+
+async def main(n: int) -> None:
+    snapshot = snapshot_name()
+    print(f"snapshot base: {snapshot}  (digest suffix added by DaytonaRuntime)")
+
+    cold = await wave(n, "no pool")
+
+    print("\ncreating warm pool...")
+    from daytona import AsyncDaytona
+
+    async with AsyncDaytona() as d:
+        page = await d.snapshot.list()
+        built = next(s.name for s in page.items if s.name.startswith(snapshot))
+    print(f"pool target snapshot: {built}")
+
+    # Match the pool's region to where the runtime actually creates sandboxes.
+    provider = DaytonaRuntime(built, image=Image.from_dockerfile("Dockerfile.hud"))
+    async with provider(fix_calc(variant=0)) as probe:
+        target = await pool.region_of(probe.params["instance_id"])
+    print(f"region: {target}")
+
+    try:
+        filled = await fill_pool(built, n, target)
+        print(f"pool reported {filled}/{n}\n")
+        warm = await wave(n, "warm pool")
+    finally:
+        await drop_pool(built)
+
+    if cold["p50"] and warm["p50"]:
+        delta = (cold["p50"] - warm["p50"]) / cold["p50"] * 100
+        print(f"\np50 {cold['p50']}s -> {warm['p50']}s  ({delta:+.0f}%)")
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--n", type=int, default=8)
+    a = p.parse_args()
+    asyncio.run(main(a.n))
diff --git a/cookbooks/daytona-rl/bench/warmpool_repro.py b/cookbooks/daytona-rl/bench/warmpool_repro.py
new file mode 100644
index 000000000..cf86bf5a2
--- /dev/null
+++ b/cookbooks/daytona-rl/bench/warmpool_repro.py
@@ -0,0 +1,142 @@
+"""Warm pool A/B: sequential vs concurrent sandbox creation.
+
+    export DAYTONA_API_KEY=...
+    uv run --with daytona python warmpool_repro.py
+    uv run --with daytona python warmpool_repro.py --snapshot my-snapshot --n 8
+
+Measures how long `daytona.create` takes from an existing snapshot, four ways:
+one-at-a-time and N-at-once, each with and without a warm pool of size N.
+
+Creates a warm pool on the chosen snapshot and deletes it at the end, along with
+every sandbox it starts. Defaults to a stock Daytona base image so it runs with
+no setup.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import os
+import statistics
+import time
+
+from daytona import AsyncDaytona, CreateSandboxFromSnapshotParams
+from daytona_api_client_async import ApiClient, Configuration, WarmPoolsApi
+from daytona_api_client_async.models.create_warm_pool import CreateWarmPool
+
+HOST = "https://app.daytona.io/api"
+
+
+def _api() -> ApiClient:
+    key = os.environ.get("DAYTONA_API_KEY")
+    if not key:
+        raise SystemExit("set DAYTONA_API_KEY")
+    return ApiClient(Configuration(host=HOST, access_token=key))
+
+
+async def _create_once(daytona: AsyncDaytona, snapshot: str, ephemeral: bool) -> float:
+    """Time one create, then delete the sandbox."""
+    started = time.perf_counter()
+    sandbox = await daytona.create(
+        CreateSandboxFromSnapshotParams(
+            snapshot=snapshot, ephemeral=ephemeral, auto_stop_interval=0
+        ),
+        timeout=180,
+    )
+    elapsed = time.perf_counter() - started
+    await daytona.delete(sandbox)
+    return elapsed
+
+
+async def sequential(snapshot: str, reps: int, ephemeral: bool) -> list[float]:
+    out = []
+    async with AsyncDaytona() as daytona:
+        for _ in range(reps):
+            out.append(await _create_once(daytona, snapshot, ephemeral))
+            await asyncio.sleep(15)
+    return out
+
+
+async def burst(snapshot: str, n: int, ephemeral: bool) -> list[float]:
+    async with AsyncDaytona() as daytona:
+        return sorted(
+            await asyncio.gather(*(_create_once(daytona, snapshot, ephemeral) for _ in range(n)))
+        )
+
+
+async def pool_size(snapshot: str) -> int | None:
+    async with _api() as api:
+        pools = await WarmPoolsApi(api).list_warm_pools()
+        return next((p.current_size for p in pools if p.snapshot == snapshot), None)
+
+
+async def make_pool(snapshot: str, n: int, region: str, timeout_s: int = 600) -> int | None:
+    async with _api() as api:
+        await WarmPoolsApi(api).create_warm_pool(
+            CreateWarmPool(snapshot=snapshot, pool=n, target=region)
+        )
+    started = time.perf_counter()
+    while time.perf_counter() - started < timeout_s:
+        current = await pool_size(snapshot)
+        if current is not None and current >= n:
+            print(f"  pool filled {current}/{n} in {time.perf_counter() - started:.0f}s")
+            return current
+        await asyncio.sleep(5)
+    return await pool_size(snapshot)
+
+
+async def drop_pool(snapshot: str) -> None:
+    async with _api() as api:
+        pools = WarmPoolsApi(api)
+        for pool in await pools.list_warm_pools():
+            if pool.snapshot == snapshot:
+                await pools.delete_warm_pool(pool.id)
+
+
+def show(label: str, values: list[float]) -> None:
+    print(
+        f"  {label:<22} median {statistics.median(values):5.2f}s   "
+        f"[{', '.join(f'{v:.2f}' for v in values)}]"
+    )
+
+
+async def main(snapshot: str, n: int, region: str, ephemeral: bool) -> None:
+    print(f"snapshot={snapshot}  n={n}  region={region}  ephemeral={ephemeral}\n")
+
+    print("without a warm pool")
+    cold_seq = await sequential(snapshot, 3, ephemeral)
+    show(f"sequential x3", cold_seq)
+    cold_burst = await burst(snapshot, n, ephemeral)
+    show(f"{n} at once", cold_burst)
+
+    print("\ncreating warm pool...")
+    filled = await make_pool(snapshot, n, region)
+    if not filled:
+        print("  pool never filled — aborting")
+        await drop_pool(snapshot)
+        return
+
+    try:
+        print("\nwith a warm pool")
+        warm_seq = await sequential(snapshot, 3, ephemeral)
+        show(f"sequential x3", warm_seq)
+        warm_burst = await burst(snapshot, n, ephemeral)
+        show(f"{n} at once", warm_burst)
+    finally:
+        await drop_pool(snapshot)
+        print("\npool deleted")
+
+    seq_gain = statistics.median(cold_seq) - statistics.median(warm_seq)
+    burst_gain = statistics.median(cold_burst) - statistics.median(warm_burst)
+    print(f"\nsequential improved by {seq_gain:+.2f}s, {n}-at-once improved by {burst_gain:+.2f}s")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--snapshot", default="daytonaio/sandbox:0.7.0")
+    parser.add_argument("--n", type=int, default=8)
+    parser.add_argument("--region", default="eu")
+    parser.add_argument("--ephemeral", action="store_true", default=True)
+    parser.add_argument("--no-ephemeral", dest="ephemeral", action="store_false")
+    args = parser.parse_args()
+    asyncio.run(main(args.snapshot, args.n, args.region, args.ephemeral))
diff --git a/cookbooks/daytona-rl/bugs.py b/cookbooks/daytona-rl/bugs.py
new file mode 100644
index 000000000..0539732ac
--- /dev/null
+++ b/cookbooks/daytona-rl/bugs.py
@@ -0,0 +1,189 @@
+"""Generate graded variants of the calc debugging task.
+
+Each function is self-contained — no function calls another — so an injected bug
+fails only its own tests. That keeps difficulty a function of *how many* bugs are
+present rather than of which dependency chain happened to break.
+
+A variant is a deterministic subset of BUGS of size k, k cycling 2/3/4.
+"""
+
+from __future__ import annotations
+
+HEADER = '"""A tiny stats helper."""\n\nfrom collections import Counter\n'
+
+FUNCS: dict[str, dict[str, str]] = {
+    "mean": {
+        "correct": """
+def mean(xs):
+    return sum(xs) / len(xs)
+""",
+        "buggy": """
+def mean(xs):
+    return sum(xs) // len(xs)
+""",
+        "tests": """
+def test_mean():
+    assert mean([1, 2, 3, 4]) == 2.5
+""",
+    },
+    "median": {
+        "correct": """
+def median(xs):
+    s = sorted(xs)
+    n = len(s)
+    mid = n // 2
+    if n % 2:
+        return s[mid]
+    return (s[mid - 1] + s[mid]) / 2
+""",
+        "buggy": """
+def median(xs):
+    s = sorted(xs)
+    return s[len(s) // 2]
+""",
+        "tests": """
+def test_median_odd():
+    assert median([3, 1, 2]) == 2
+
+
+def test_median_even():
+    assert median([1, 2, 3, 4]) == 2.5
+""",
+    },
+    "variance": {
+        "correct": """
+def variance(xs):
+    m = sum(xs) / len(xs)
+    return sum((x - m) ** 2 for x in xs) / (len(xs) - 1)
+""",
+        "buggy": """
+def variance(xs):
+    m = sum(xs) / len(xs)
+    return sum((x - m) ** 2 for x in xs) / len(xs)
+""",
+        "tests": """
+def test_variance_is_sample_variance():
+    assert variance([1, 2, 3, 4]) == pytest.approx(1.6666666666666667)
+""",
+    },
+    "stdev": {
+        "correct": """
+def stdev(xs):
+    m = sum(xs) / len(xs)
+    return (sum((x - m) ** 2 for x in xs) / (len(xs) - 1)) ** 0.5
+""",
+        "buggy": """
+def stdev(xs):
+    m = sum(xs) / len(xs)
+    return sum(abs(x - m) for x in xs) / len(xs)
+""",
+        "tests": """
+def test_stdev_is_sample_stdev():
+    assert stdev([1, 2, 3, 4]) == pytest.approx(1.2909944487358056)
+""",
+    },
+    "percentile": {
+        "correct": """
+def percentile(xs, p):
+    s = sorted(xs)
+    k = (len(s) - 1) * p
+    lo = int(k)
+    hi = min(lo + 1, len(s) - 1)
+    if lo == hi:
+        return float(s[lo])
+    return s[lo] + (s[hi] - s[lo]) * (k - lo)
+""",
+        "buggy": """
+def percentile(xs, p):
+    s = sorted(xs)
+    return s[int(round((len(s) - 1) * p))]
+""",
+        "tests": """
+def test_percentile_interpolates():
+    assert percentile([1, 2, 3, 4], 0.25) == pytest.approx(1.75)
+
+
+def test_percentile_endpoints():
+    assert percentile([1, 2, 3, 4], 0.0) == pytest.approx(1.0)
+    assert percentile([1, 2, 3, 4], 1.0) == pytest.approx(4.0)
+""",
+    },
+    "mode": {
+        "correct": """
+def mode(xs):
+    counts = Counter(xs)
+    top = max(counts.values())
+    return min(x for x, c in counts.items() if c == top)
+""",
+        "buggy": """
+def mode(xs):
+    return Counter(xs).most_common(1)[0][0]
+""",
+        "tests": """
+def test_mode_breaks_ties_by_smallest():
+    assert mode([3, 1, 1, 3]) == 1
+""",
+    },
+    "spread": {
+        "correct": """
+def spread(xs):
+    return max(xs) - min(xs)
+""",
+        "buggy": """
+def spread(xs):
+    return max(xs) - min(xs) + 1
+""",
+        "tests": """
+def test_spread():
+    assert spread([1, 2, 3, 4]) == 3
+""",
+    },
+    "cv": {
+        "correct": """
+def cv(xs):
+    m = sum(xs) / len(xs)
+    sd = (sum((x - m) ** 2 for x in xs) / (len(xs) - 1)) ** 0.5
+    return sd / m
+""",
+        "buggy": """
+def cv(xs):
+    m = sum(xs) / len(xs)
+    sd = (sum((x - m) ** 2 for x in xs) / (len(xs) - 1)) ** 0.5
+    return m / sd
+""",
+        "tests": """
+def test_cv():
+    assert cv([1, 2, 3, 4]) == pytest.approx(0.5163977794943222)
+""",
+    },
+}
+
+NAMES = list(FUNCS)
+K_CYCLE = (4, 5, 6)
+
+
+def broken_for(variant: int) -> list[str]:
+    """Deterministic subset of function names to break for *variant*."""
+    k = K_CYCLE[variant % len(K_CYCLE)]
+    start = (variant * 3) % len(NAMES)
+    return [NAMES[(start + i) % len(NAMES)] for i in range(k)]
+
+
+def build(variant: int) -> tuple[str, str, list[str]]:
+    """Return (calc.py source, test_calc.py source, names broken)."""
+    broken = broken_for(variant)
+    calc = HEADER + "".join(
+        "\n" + FUNCS[name]["buggy" if name in broken else "correct"].strip("\n") + "\n"
+        for name in NAMES
+    )
+    tests = (
+        "import pytest\n\nfrom calc import "
+        + ", ".join(sorted(NAMES))
+        + "\n"
+        + "".join("\n" + FUNCS[name]["tests"].strip("\n") + "\n" for name in NAMES)
+    )
+    return calc, tests, broken
+
+
+def test_count() -> int:
+    return sum(f["tests"].count("def test_") for f in FUNCS.values())
diff --git a/cookbooks/daytona-rl/env.py b/cookbooks/daytona-rl/env.py
new file mode 100644
index 000000000..3e44be5d4
--- /dev/null
+++ b/cookbooks/daytona-rl/env.py
@@ -0,0 +1,73 @@
+"""smoke - HUD environment with a real coding task graded by pytest.
+
+Spine of the HUD x Daytona guide: one shell capability, one buggy repo, one
+pytest grader. Runs identically under LocalRuntime and DaytonaRuntime.
+
+Reward is binary — every test passes or the rollout scores zero.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import re
+import sys
+import tempfile
+from pathlib import Path
+
+from hud.environment import Environment
+
+import bugs
+
+env = Environment(name="smoke")
+
+WORKSPACE = Path(tempfile.mkdtemp(prefix="calc-", dir="/tmp"))
+ws = env.workspace(WORKSPACE, network=True)
+
+TOTAL_TESTS = bugs.test_count()
+
+
+def seed_bug(variant: int) -> list[str]:
+    calc, tests, broken = bugs.build(variant)
+    (WORKSPACE / "calc.py").write_text(calc)
+    (WORKSPACE / "test_calc.py").write_text(tests)
+    return broken
+
+
+async def run_pytest() -> tuple[int, int]:
+    proc = await asyncio.create_subprocess_exec(
+        sys.executable,
+        "-m",
+        "pytest",
+        "-q",
+        "--no-header",
+        "-p",
+        "no:cacheprovider",
+        cwd=str(WORKSPACE),
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.STDOUT,
+    )
+    out, _ = await proc.communicate()
+    match = re.search(r"(\d+) passed", out.decode("utf-8", "replace"))
+    return (int(match.group(1)) if match else 0), TOTAL_TESTS
+
+
+@env.template(id="fix_calc")
+async def fix_calc(variant: int = 0):
+    seed_bug(variant)
+    yield (
+        f"`pytest` fails in `{WORKSPACE}`. Fix `calc.py` so it passes. Don't edit `test_calc.py`."
+    )
+    passed, total = await run_pytest()
+    yield 1.0 if passed == total else 0.0
+
+
+async def test() -> None:
+    from hud import LocalRuntime
+    from hud.agents.claude import ClaudeAgent
+
+    job = await fix_calc(variant=0).run(ClaudeAgent(), runtime=LocalRuntime(__file__))
+    print("reward:", job.reward)
+
+
+if __name__ == "__main__":
+    asyncio.run(test())
diff --git a/cookbooks/daytona-rl/pool.py b/cookbooks/daytona-rl/pool.py
new file mode 100644
index 000000000..6d39e08ea
--- /dev/null
+++ b/cookbooks/daytona-rl/pool.py
@@ -0,0 +1,99 @@
+"""Daytona warm pools: create one, wait until it is genuinely full, drop it.
+
+Warm pools are absent from the high-level `daytona` SDK — they live in the
+generated client (`WarmPoolsApi`). The subtlety worth knowing: a pool's own
+`current_size` reports full roughly 12s before the sandboxes exist, so
+`wait_full` counts real sandboxes instead. Counting them needs
+`include_warm=True`; unclaimed pool members are excluded from `list_sandboxes`
+by default, which makes a naive count return 0 forever.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import os
+import time
+
+from daytona_api_client_async import ApiClient, Configuration, SandboxApi, WarmPoolsApi
+from daytona_api_client_async.models.create_warm_pool import CreateWarmPool
+
+HOST = "https://app.daytona.io/api"
+LIVE_STATES = {"started", "running"}
+
+
+def _api() -> ApiClient:
+    return ApiClient(Configuration(host=HOST, access_token=os.environ["DAYTONA_API_KEY"]))
+
+
+def _state(sandbox) -> str:
+    return str(getattr(sandbox.state, "value", sandbox.state)).lower()
+
+
+async def _live_ids(snapshot: str, *, include_warm: bool) -> set[str]:
+    """Live sandbox ids on *snapshot*, paginated (`limit` caps at 200)."""
+    ids: set[str] = set()
+    cursor = None
+    async with _api() as api:
+        sandboxes = SandboxApi(api)
+        while True:
+            page = await sandboxes.list_sandboxes(
+                include_warm=include_warm, snapshots=[snapshot], limit=200, cursor=cursor
+            )
+            items = page.items if hasattr(page, "items") else page
+            ids.update(i.id for i in items if _state(i) in LIVE_STATES)
+            cursor = getattr(page, "next_cursor", None)
+            if not cursor or not items:
+                return ids
+
+
+async def unclaimed(snapshot: str) -> int:
+    """Warm sandboxes nobody has claimed yet, as a set difference so it cannot
+    overshoot when a sandbox is deleted between the two listings."""
+    warm = await _live_ids(snapshot, include_warm=True)
+    claimed = await _live_ids(snapshot, include_warm=False)
+    return len(warm - claimed)
+
+
+async def region_of(sandbox_id: str) -> str:
+    """The region a sandbox actually landed in.
+
+    DaytonaRuntime creates sandboxes without a target, so they go to the org
+    default. Daytona only serves a pooled sandbox when the create request's
+    region matches the pool's, so a hardcoded region silently bills for a pool
+    nobody claims. Read it off a real sandbox instead.
+    """
+    async with _api() as api:
+        sandbox = await SandboxApi(api).get_sandbox(sandbox_id)
+    return str(sandbox.target)
+
+
+async def create(snapshot: str, size: int, target: str) -> None:
+    async with _api() as api:
+        await WarmPoolsApi(api).create_warm_pool(
+            CreateWarmPool(snapshot=snapshot, pool=size, target=target)
+        )
+    print(f"  pool created: {snapshot} size={size} target={target}", flush=True)
+
+
+async def drop(snapshot: str) -> None:
+    async with _api() as api:
+        pools = WarmPoolsApi(api)
+        for pool in await pools.list_warm_pools():
+            if pool.snapshot == snapshot:
+                await pools.delete_warm_pool(pool.id)
+                print(f"  deleted warm pool {pool.id}", flush=True)
+
+
+async def wait_full(snapshot: str, size: int, timeout_s: float = 1800, poll_s: float = 5) -> int:
+    """Block until *size* sandboxes really exist. Returns how many."""
+    started = time.perf_counter()
+    last = -1
+    while time.perf_counter() - started < timeout_s:
+        live = await unclaimed(snapshot)
+        if live != last:
+            print(f"  pool: {live}/{size} @ {time.perf_counter() - started:.0f}s", flush=True)
+            last = live
+        if live >= size:
+            return live
+        await asyncio.sleep(poll_s)
+    return last
diff --git a/cookbooks/daytona-rl/pyproject.toml b/cookbooks/daytona-rl/pyproject.toml
new file mode 100644
index 000000000..ceeba8508
--- /dev/null
+++ b/cookbooks/daytona-rl/pyproject.toml
@@ -0,0 +1,8 @@
+[project]
+name = "smoke"
+version = "0.1.0"
+requires-python = ">=3.11"
+dependencies = ["hud[daytona]", "pytest"]
+
+[tool.uv]
+package = false
diff --git a/cookbooks/daytona-rl/snapshot.py b/cookbooks/daytona-rl/snapshot.py
new file mode 100644
index 000000000..e57a9d257
--- /dev/null
+++ b/cookbooks/daytona-rl/snapshot.py
@@ -0,0 +1,23 @@
+"""Content-addressed Daytona snapshot names.
+
+`DaytonaRuntime(name, image=...)` resolves by calling `snapshot.get(name)` and
+only builds when that 404s — so editing env.py and reusing the name silently
+runs the *old* env in the sandbox. Naming the snapshot after a hash of what goes
+into it makes staleness impossible instead of merely remembered.
+"""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+
+# uv.lock is in here because Dockerfile.hud installs from it (`uv sync --frozen`):
+# a dependency bump with no source change still produces a different image.
+INPUTS = ("env.py", "bugs.py", "Dockerfile.hud", "pyproject.toml", "uv.lock")
+
+
+def snapshot_name(prefix: str = "hud-smoke") -> str:
+    digest = hashlib.sha256()
+    for name in INPUTS:
+        digest.update(Path(name).read_bytes())
+    return f"{prefix}-{digest.hexdigest()[:8]}"
diff --git a/cookbooks/daytona-rl/tasks.py b/cookbooks/daytona-rl/tasks.py
new file mode 100644
index 000000000..98440f34e
--- /dev/null
+++ b/cookbooks/daytona-rl/tasks.py
@@ -0,0 +1,5 @@
+"""Tasks for smoke — run with: hud eval tasks.py claude-sonnet-5."""
+
+from env import fix_calc
+
+tasks = [fix_calc(variant=0)]
diff --git a/cookbooks/daytona-rl/train.py b/cookbooks/daytona-rl/train.py
new file mode 100644
index 000000000..d991a4a58
--- /dev/null
+++ b/cookbooks/daytona-rl/train.py
@@ -0,0 +1,216 @@
+"""The published training run: 10 steps of GRPO, each a 128-wide burst on Daytona.
+
+    uv run train.py --steps 10
+
+Shape per step: 16 bug variants x 8 attempts = 128 rollouts against a warm pool
+of 128 (pool = batch, the sizing rule the guide gives). Variants 0-15 only;
+16-23 are held out so the before/after pass rate is measured on bugs the model
+never trained on.
+
+Records what the guide needs: pass rate per step (the curve), within-group reward
+spread (what GRPO actually consumes), wall-clock split rollouts vs training, and
+the count of rollouts that never launched — an infra failure arrives as a
+reward-0.0 run with no tokens, which is indistinguishable from a model failure
+unless it is counted separately.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import contextlib
+import json
+import resource
+import signal
+import statistics
+import time
+from pathlib import Path
+
+from daytona import Image
+from hud import TrainingClient
+from hud.agents import create_agent
+from hud.agents.types import AgentStep
+from hud.eval import DaytonaRuntime, Job, Taskset
+
+from env import fix_calc
+from snapshot import snapshot_name
+import pool
+
+MODEL = "daytona-calc-3"
+SNAPSHOT = snapshot_name()
+TRAIN_VARIANTS = range(16)  # 16-23 held out for evaluation
+
+
+def _samples(runs: list) -> list:
+    return [
+        sample
+        for run in runs
+        for sample in run.trace.collect(
+            lambda s: s.sample if isinstance(s, AgentStep) and s.sample else None
+        )
+    ]
+
+
+def _group_spread(runs: list) -> float | None:
+    """Mean within-group reward stdev — the quantity GRPO turns into advantage."""
+    groups: dict[str, list[float]] = {}
+    for run in runs:
+        gid = getattr(run, "group_id", None)
+        if gid is not None:
+            groups.setdefault(gid, []).append(run.reward or 0.0)
+    spreads = [statistics.pstdev(v) for v in groups.values() if len(v) > 1]
+    return round(statistics.mean(spreads), 4) if spreads else None
+
+
+async def main(
+    *, steps: int, group: int, lr: float, concurrent: int, chunk: int, out: Path
+) -> None:
+    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+    want = concurrent * 16 + 512
+    if soft < want:
+        resource.setrlimit(resource.RLIMIT_NOFILE, (min(want, hard), hard))
+
+    agent = create_agent(
+        MODEL,
+        completion_kwargs={"max_tokens": 2048, "extra_body": {"return_token_ids": True}},
+    )
+    trainer = TrainingClient(MODEL)
+    taskset = Taskset("calc", [fix_calc(variant=v) for v in TRAIN_VARIANTS])
+    runtime = DaytonaRuntime(SNAPSHOT, image=Image.from_dockerfile("Dockerfile.hud"))
+
+    # A killed process skips the finally and strands the whole pool (128 vCPU),
+    # so cancel the loop on a signal and let the finally run.
+    loop = asyncio.get_running_loop()
+    stopping = asyncio.Event()
+    for sig in (signal.SIGINT, signal.SIGTERM):
+        with contextlib.suppress(NotImplementedError):
+            loop.add_signal_handler(sig, stopping.set)
+
+    # Resume-safe: a killed run loses its pool but keeps its checkpoints, so
+    # re-invoking must append to the curve rather than restart it at step 0.
+    history: list[dict] = json.loads(out.read_text()) if out.exists() else []
+    step_offset = (history[-1]["step"] + 1) if history else 0
+    if step_offset:
+        print(f"resuming after step {step_offset - 1} ({len(history)} recorded)", flush=True)
+    # Daytona rejects a warm pool whose snapshot does not exist yet (400), and
+    # DaytonaRuntime only builds the snapshot on its first acquisition — so take
+    # one sandbox first to force the build, then pool against it.
+    print("building snapshot (first run only)...", flush=True)
+    async with runtime(fix_calc(variant=0)) as warmup:
+        # The pool must sit in the same region the runtime creates sandboxes in,
+        # or Daytona never serves from it. The runtime sets no target, so take
+        # the region from this sandbox rather than assuming one.
+        target = await pool.region_of(warmup.params["instance_id"])
+    print(f"region: {target}", flush=True)
+
+    await pool.drop(SNAPSHOT)
+    await pool.create(SNAPSHOT, concurrent, target)
+    try:
+        filled = await pool.wait_full(SNAPSHOT, concurrent)
+        if filled < concurrent:
+            print(
+                f"WARNING: pool reached {filled}/{concurrent}; rollouts above that "
+                f"run cold and the pool-sized timings will not hold",
+                flush=True,
+            )
+        else:
+            print(f"pool full: {filled}/{concurrent}", flush=True)
+
+        session = await Job.start("calc-rl-daytona-published", group=group)
+        print(f"job {session.id} | model {MODEL} | variants {list(TRAIN_VARIANTS)}", flush=True)
+
+        for step in range(step_offset, step_offset + steps):
+            if stopping.is_set():
+                print("stop requested — exiting cleanly", flush=True)
+                break
+            batch_start = len(session.runs)
+
+            t0 = time.perf_counter()
+            await taskset.run(
+                agent, runtime=runtime, job=session, group=group, max_concurrent=concurrent
+            )
+            rollout_s = time.perf_counter() - t0
+            batch = session.runs[batch_start:]
+
+            samples = _samples(batch)
+            rewards = [r.reward or 0.0 for r in batch]
+            errored = sum(1 for r in batch if r.trace.is_error)
+            solved = sum(1 for r in rewards if r > 0)
+            row = {
+                "step": step,
+                "runs": len(batch),
+                "solved": solved,
+                "pass_rate": round(solved / len(batch), 4) if batch else None,
+                "errored": errored,
+                "group_spread": _group_spread(batch),
+                "samples": len(samples),
+                "tokens": sum(len(s.output_token_ids) for s in samples),
+                "rollout_s": round(rollout_s, 1),
+            }
+            print(
+                f"\nstep {step}: pass {solved}/{len(batch)} = {row['pass_rate']:.1%} | "
+                f"errored {errored} | spread {row['group_spread']} | "
+                f"rollout {rollout_s:.0f}s | {row['tokens']} tok",
+                flush=True,
+            )
+            if not samples:
+                print("  !! no token-level samples — cannot train", flush=True)
+                history.append(row | {"aborted": "no_samples"})
+                break
+
+            # 128 runs with tokens inline is ~37MB and the endpoint rejects it
+            # (413). Several forward_backward calls accumulate into one
+            # optim_step, so send the batch in group-aligned chunks.
+            t1 = time.perf_counter()
+            loss_sum, datums = 0.0, 0
+            for i in range(0, len(batch), chunk):
+                part = batch[i : i + chunk]
+                fb = await trainer.forward_backward(
+                    part, loss_fn="importance_sampling", group_size=group
+                )
+                loss_sum += fb.metrics.get("loss:sum") or 0.0
+                datums += fb.num_datums
+            row["loss"] = round(loss_sum, 4)
+            row["datums"] = datums
+            row["chunks"] = -(-len(batch) // chunk)
+
+            if len({round(r, 6) for r in rewards}) == 1:
+                row["train_s"] = round(time.perf_counter() - t1, 1)
+                row["optim_step"] = None
+                print(f"  no reward spread in batch — skipping optim_step", flush=True)
+            else:
+                result = await trainer.optim_step(learning_rate=lr)
+                row["train_s"] = round(time.perf_counter() - t1, 1)
+                row["optim_step"] = result.step
+                print(
+                    f"  fwd/bwd+optim {row['train_s']}s | loss {row['loss']} | "
+                    f"optim step {result.step}",
+                    flush=True,
+                )
+            history.append(row)
+            out.write_text(json.dumps(history, indent=1))
+    finally:
+        await pool.drop(SNAPSHOT)
+        out.write_text(json.dumps(history, indent=1))
+        print(f"\nwrote {out} ({len(history)} steps); pool dropped", flush=True)
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("--steps", type=int, default=10)
+    p.add_argument("--group", type=int, default=8)
+    p.add_argument("--concurrent", type=int, default=128)
+    p.add_argument("--learning-rate", type=float, default=1e-5)
+    p.add_argument("--chunk", type=int, default=16)
+    p.add_argument("--out", default="train_run.json")
+    a = p.parse_args()
+    asyncio.run(
+        main(
+            steps=a.steps,
+            group=a.group,
+            lr=a.learning_rate,
+            concurrent=a.concurrent,
+            chunk=a.chunk,
+            out=Path(a.out),
+        )
+    )
diff --git a/cookbooks/daytona-rl/uv.lock b/cookbooks/daytona-rl/uv.lock
new file mode 100644
index 000000000..39aaa7e7d
--- /dev/null
+++ b/cookbooks/daytona-rl/uv.lock
@@ -0,0 +1,3288 @@
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