The source introduces Reinforcement Pre-Training (RPT), a novel approach that redefines next-token prediction in large language models (LLMs) as a verifiable reasoning task. Unlike traditional methods relying on costly human feedback or limited annotated data, RPT uses reinforcement learning (RL) with intrinsic, rule-based rewards derived directly from the pre-training corpus. This method incentivizes LLMs to engage in a deeper "chain-of-thought" reasoning process before predicting the next token, transforming vast unannotated text into a large-scale RL dataset. The paper demonstrates that RPT improves next-token prediction accuracy, enhances reasoning abilities on various benchmarks, and provides a stronger foundation for subsequent RL fine-tuning, suggesting a promising new direction for developing more capable LLMs.

https://arxiv.org/pdf/2506.08007