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Scaling prompt learning by 17x without sacrificing accuracy is now possible, unlocking efficient self-improvement for LLM agents.
Achieve significant cost savings in LLM reinforcement learning by overlapping rollout generation, dissemination, and training with a framework that tolerates bounded policy staleness.
