fix(qwen3-ar): deterministic sampling seeds and trainer-state correctness#208
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CjhHa1 wants to merge 3 commits into
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fix(qwen3-ar): deterministic sampling seeds and trainer-state correctness#208CjhHa1 wants to merge 3 commits into
CjhHa1 wants to merge 3 commits into
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…ness Stabilize SGLang seeded sampling with BLAKE2b-derived per-sample seeds and driver-side sample_id validation before DP scatter. Fix optimizer_step tracking when W&B is disabled and scalar tensor checksum tracking when W&B is disabled and scalar tensor checksum crashes.
Remove documentation and test additions so the PR contains only the requested production changes.
Keep the pull request focused on runtime correctness fixes while preserving profiling work locally.
celve
requested changes
Jul 15, 2026
| enable_lora: false | ||
| # Keep production sampling on SGLang's fast default backend. The | ||
| # profiling launcher overrides this to true together with a fixed seed. | ||
| enable_deterministic_inference: false |
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I think we shouldn’t call this deterministic, since that usually implies batch‑invariant behavior.
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Summary
sampling_seedfrom(base_seed, sample_id)via BLAKE2b so RNG assignment is stable across DP sharding, request reordering, and asynchronous completion; validate globally uniquesample_idsbefore rollout.optimizer_stepwhen telemetry is disabled, matching live logging semantics so checkpoint resume state remains correct.fingerprint_tensorby reshaping before the uint8 view.seed=Noneandenable_deterministic_inference: false.Validation