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Stale rollouts can introduce a significant bias in learning rates, fundamentally altering the stability landscape of asynchronous RLHF systems.
Achieve significant cost savings in LLM reinforcement learning by overlapping rollout generation, dissemination, and training with a framework that tolerates bounded policy staleness.
