Each change targets a failure that produced no usable signal at the point of
cause: weights that never transfer, metadata that fails in another process, and
a performance cliff recorded only in a docstring.

unscan_layers had no tests, and it is the piece that decides whether trainer and
sampler tensor names agree. Both sides name tensors with jax.tree_util.keystr,
and nothing cross-checks the two sets: raiden_handler._validate_metadata only
validates one manifest's internal consistency (mesh rank, duplicate
variable/layer keys, sharding specs). A naming error therefore surfaces as
weights that silently never transfer. Two cases pin non-obvious invariants:
unscan_layers returns a plain nested dict while the sampler binds an nnx.State,
and keystr renders those identically only because the transform rewraps leaves
in nnx.Param -- dropping that rewrap would rename every tensor ("['k']" vs
"['k'].value"); and an already-unscanned state must raise, since without that
guard it would return unchanged and bind under scanned names.

prepare_weight_sync returned empty metadata on two paths: a missing
raiden_synchronizer (warning-level) and an unrecognised staging_transport (no
log at all). Neither is silent end to end -- WeightSyncCoordinator rejects an
empty side -- but the failure lands far from the cause, surfacing in another
process as "metadata collection returned an empty side", a count that never
names the missing module or the bad transport. The import case is the common one
rather than a corner: raiden_synchronizer ships only on tunix's Raiden branch,
so any released tunix takes it. Both now raise where the cause is known, with
the ImportError chained so the traceback keeps the module name. Because
staging_transport defaults to "raiden", this also reaches callers that never
asked for it, so the engine e2e test now probes for the synchronizer the way the
engine does -- exercising the real staging path where Raiden exists and the
documented failure where it does not.

_batch_data_shardings falls back to replicating the batch dimension when it does
not divide the batch axis's mesh size. That is correct -- every device along the
axis computes the whole micro-batch -- but it costs N times the work a sharded
one would do there. An invisible performance cliff is harder to notice than a
wrong number, because XLA's caching can make it look like nothing worse than a
slow run; the file already warns once per instance when a signature half cannot
be compared, and this extends that treatment. Warned once per instance rather
than per leaf, since the check runs under a tree_map over every loss input and
they normally share a batch dim. A sequence-packed micro-batch is always size 1
and has no alternative, so the message says the fallback may well be deliberate.

Verification. The unscan suite has teeth: renaming the emitted key from
layers_{i} to layer_{i} fails 5 of its 11 tests, including the name-equality
one. Marked post_training and left in tests/unit, which is already in
cpu-post-training-unit's path list, so the marker alone routes it -- tests/ and
tests/integration are not in that list, which is how the engine tests once ended
up collected by no job at all; collection confirms 11 tests in
cpu-post-training-unit and 0 in cpu-unit. The staging and sharding tests fail
without their respective changes. The sharding tests stub both the data spec and
the axis size: a single-device test mesh returns None in the batch position,
making the branch unreachable as configured, and an earlier draft asserted
`spec[0] is None` and passed without running the code under test at all.

…rics recorder, and add cache invalidation

- Handle nested vllm dict/object in HyperParameters for use_weight_converter and rollout_backend
- Restore self._metrics_recorder = metrics_module.MetricsRecorder() in MaxTextTrainingEngine
- Invalidate staged metadata cache in release_weight_sync()
- Gate unroll_gemma_scanned_weights by Gemma model identity in MaxTextVllmSampler
- Set default num_lanes=128 in compute_padded_moe_mlp_dim
- Clarify memory lifecycle in WeightConverter convert docstrings and enhance test_case_5 memory profiling

…ase root in streaming converter

- Revert streaming piece-by-piece conversion in MaxTextTrainingEngine to single-piece convert and bind
- Restore base root prefix in MaxTextToMaxTextConverter.convert_streaming()
- Update prepare_weight_sync_test suite to reflect single sync instance

… and consolidate sync instances

- Under Pathways (JAX_PLATFORMS=proxy), require weight_synchronizer_ffi to avoid client host OOM
- Consolidate to single RaidenSynchronizer instance in MaxTextTrainingEngine
- Add reclaim_host_memory() utility invoking gc.collect() and malloc_trim(0)
- Add weight_sync_debug flag to HyperParameters config
- Update unit tests across maxtext_engine, prepare_weight_sync, and weight_converter