Standard bf16 Rounding Discards 99% of Your RL Policy Updates
If you run reinforcement learning post-training on language models using frameworks like verl, TRL, or OpenRLHF, your rollout engine is probably serving weights that are dozens of optimizer steps out of date. The issue is not network latency or worker contention. It comes from the cast that convert…
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- 2026-11-02 16:29 · DEV Community — Machine Learning
Standard bf16 Rounding Discards 99% of Your RL Policy Updates