[Paper] From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space

Summary

This paper proposes a novel approach to enhance LLM reasoning by shifting from optimizing conditional distributions P(y|x) in reinforcement learning to optimizing the marginal distribution P(y) within the pre-train space. This method aims to overcome the limitations of current RL techniques by directly encoding reasoning ability and preserving broad exploration capacity, addressing the bottleneck of static pre-training corpora.

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