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Computer Science > Computation and Language

arXiv:2506.08007 (cs)
[Submitted on 9 Jun 2025]

Title:Reinforcement Pre-Training

Authors:Qingxiu Dong, Li Dong, Yao Tang, Tianzhu Ye, Yutao Sun,
Zhifang Sui, Furu Wei
View a PDF of the paper titled Reinforcement Pre-Training, by Qingxiu
Dong and 6 other authors
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    Abstract: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.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2506.08007 [cs.CL]
          (or arXiv:2506.08007v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2506.08007
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Li Dong [view email]
[v1] Mon, 9 Jun 2025 17:59:53 UTC (1,234 KB)
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