Phase 2 results (`scripts/benchmarks/results/grpo_equivalence.md`):
- vLLM: 74.4s train, 4.14s median step, 158 GB peak (100%)
- unsloth_fi_false: 355s train, 23.95s median, 10.7 GB peak (17%)
- cb_paged (via sdpa_paged load): 466s train, 36s median, 55.6 GB peak (11.5%)
Coherence gate passes on all three backends: losses finite, rewards in the
expected early-GRPO range, KL trajectories qualitatively matched between
vLLM and unsloth_fi_false in [0, 0.015]. Memory story is striking:
unsloth_fi_false uses 15x less memory than vLLM.
qwen3_grpo_unified.py fixes:
- Auto-adjust per_device_train_batch_size -> num_generations for vanilla-HF
backends (Unsloth's loader does this automatically; TRL on the HF path
doesn't and crashes on the divisibility check).
- cb_paged now loads with sdpa_paged (not paged_attention). The FA4
paged_attention kernel requires cu_seq_lens_q on every forward, but the
GRPO training forward feeds a dense batch without them. sdpa_paged
gracefully falls back to plain SDPA in that case and still exercises the
paged path during the CB rollout.
cb_sync_driver.py fixes:
- FIFOScheduler no longer accepts manual_eviction in its signature; dropped.
- drive_until_empty used to check has_pending_requests() before calling
prepare_next_batch(), which returned False at startup because nothing had
yet been pulled from the input_queue. Now the loop drains the input queue
first and exits only when both queues + scheduler are empty.
Smoke test on GPU 1 (8 prompts, 64 tokens): eager path produces 512 correct
tokens; CUDA-graph path hangs during first-step capture (PagedAttentionCache
probably allocates on first use). Tracked for the next commit.
5f3e1c98df