- split KVCache into phase-specific PrefillKVCache/DecodeKVCache types selected by start_pos
- unify steady-state detection in TaskCacheManager
- guard decode steady-state reuse with the cached task signature so recycled req slots cannot replay a prior generation's tokens and positions
- collapse attention backend fwd_decode/fwd_prefill into a single subclass-owned forward with a shared _check_fwd guard
- fix thread-safety gap in weight update and validate prefill inputs before KV allocation
- centralize magic constants in InferenceConfig and align docs with behavior
- extract shared helpers for dataset writers, scheduler construction, thread interleaving, hf roundtrips, and moe configs
- remove about 20 cases whose only assertions were format checks, restated declarations, fake-taxonomy duplicates, or test-local scaffolding
- strengthen weak cases into exact reference comparisons, positional mask checks, and deterministic outcomes
- replace two schedule factory smoke tests with cosine/sgdr formula assertions
- delete root-level CLI tests whose merge-priority facts are covered by tests/config/test_cli.py
- suite shrinks from 857 to 826 items; ruff format, import order, and pytest all green
- re-register the linear family with the operator dispatcher (ASTR_OPS / op_backend / resolve)
- fix bf16 gemv misaligned-address faults and element mispairing for offset weights
- reject misaligned bf16_swiglu inputs with a clear error and fall back in the backend gate
- make the rollout reuse decision, validation, and return atomic under one policy snapshot
- add the documented post-scoring rollout version check
- derive live+1 under the scheduler lock in optimizer_step via apply_weight_update(None, ...)
- reject rollout_max_policy_lag below rollout_interval - 1 at config time
- sync gemv stream-test inputs before switching streams; drop dead loader imports
Track a monotonic policy version across optimizer steps, scheduler updates, and rollout results. Serialize synchronous generation with weight acknowledgements and invalidate reusable prefix KV entries so cached samples remain attributable to the behavior policy that generated them.
- Pack prompts with a shared prefix start and attention backend into one forward.
- Select per-request final logits from cumulative query lengths.
- Cover ragged tokens, logprobs, scheduling, and documentation.
- Propagate stream closure and stop-sequence termination into scheduler cancellation
- Defer active KV release to the scheduler owner and close metrics safely
- Expose lifecycle counters and cover waiting, active, and allocation-race cleanup
- Return structured finish and error reasons for synchronous generation
- Reject failed online rollout batches instead of training on empty responses
- Verify allocation and extension failures release metrics and KV state
- Match steady-state metadata to the active task IDs
- Rebuild request mappings for cached prefix pages
- Add regressions for batch refill and prefix reuse
- Eliminate core/ directory into cache/, runtime/, network/ subpackages plus flat modules
- Split cache.py (647 lines) into cache/{buffer,strategy,pool}.py by layer
- Add explicit ContiguousStrategy, make AllocationStrategy a real ABC
- Move TaskCacheState to cache/strategy.py, drop string forward references
- Rename api/ to network/, server.py to app.py
- Move sample.py into runtime/ alongside executor and graph
- Simplify TaskCacheManager.__init__ to single pool param
- Expose pool.strategy and pool.req_pool as public properties
- Fix KVCache import in attention_backend.py (TYPE_CHECKING guard)
- Fix steady-state decode reading uninitialized position_ids on first step
- sample the first token from prefill logits without duplicating the prompt tail
- reject incomplete multi-output records before preprocessing alignment
- cover cached generation and partial DPO records with regression tests
- Remove tautological test_trainer assertions that never trained
- Drop grpo isfinite-only smokes and merge frozen-model checks via parametrize
- Merge duplicate tool_parser cases (find/streaming/factory) with parametrize
- Collapse duplicate dataset store/detect_format tests
- Remove misleading scheduler/task tests that asserted the opposite of their names
- Merge signal-handler SIGTERM/SIGINT into one parametrized case
- Drop cross-file grpo strategy duplication kept in online_strategy
- RolloutGenerator now delegates prefill/decode to InferenceScheduler.run_batch (sync API, no background thread), sharing one KV-cache code path with the inference server and eliminating O(n^2) recompute in rollout
- Add sample(return_logprobs=) and Executor.execute_decode(return_logprobs=) to expose behaviour-policy log-probs through the engine; Task gains output_logprobs
- RolloutResult now subclasses RawRollout (adds rewards only), removing duplicated fields
- RolloutRunner.__call__ returns (result, is_fresh) instead of relying on object identity, removing the fragile refresh-detection contract
- Remove O(n^2) generate_responses helper and dead code (_tokenize_prompts, unused old_model arg)
- train_context.py wires InferenceScheduler directly instead of hand-rolling SamplingPipeline
- Tests: +11 covering return_logprobs, run_batch, and KV-cache-backed rollout semantics; 404 pass
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