Reinforcement Pre-Training
Authors: name: Qingxiu Dong, name: Li Dong, name: Yao Tang, name: Tianzhu Ye, name: Yutao Sun, name: Zhifang Sui, name: Furu Wei

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.