Reinforcement Pre-Training

Qingxiu Dong, Li Dong, Yao Tang, ytz, Yutao Sun, Zhifang Sui, Furu Wei

Reinforcement Pre-Training: 265 upvotes on Hugging Face Daily Papers, #1 of 48 papers on 2025-06-10. Day-by-day upvote history.

In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling paradigm for large language models and reinforcement learning (RL). Specifically, we reframe next-token prediction as a reasoning task trained using RL, where it receives verifiable rewards for correctly predicting the next token for a given context. RPT offers a scalable method to leverage vast amounts of text data for general-purpose RL, rather than relying on domain-specific annotated answers. By incentivizing the capability of next-token reasoning, RPT significantly improves the language modeling accuracy of predicting the next tokens. Moreover, RPT provides a strong pre-trained foundation for further reinforcement fine-tuning. The scaling curves show that increased training compute consistently improves the next-token prediction accuracy. The results position RPT as an effective and promising scaling paradigm to advance language model pre-training.

Paper page on Hugging Face · arXiv

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