Reinforcement Learning on Pre-Training Data
Authors: name: Siheng Li, name: Kejiao Li, name: Zenan Xu, name: Guanhua Huang, name: Evander Yang, name: Kun Li, name: Haoyuan Wu, name: Jiajia Wu, name: Zihao Zheng, name: Chenchen Zhang, name: Kun Shi, name: Kyrierl Deng, name: Qi Yi, name: Ruibin Xiong, name: Tingqiang Xu, name: Yuhao Jiang, name: Jianfeng Yan, name: Yuyuan Zeng, name: Guanghui Xu, name: Jinbao Xue, name: Zhijiang Xu, name: Zheng Fang, name: Shuai Li, name: Qibin Liu, name: Xiaoxue Li, name: Zhuoyu Li, name: Yangyu Tao, name: Fei Gao, name: Cheng Jiang, name: Bo Chao Wang, name: Kai Liu, name: Jianchen Zhu, name: Wai Lam, name: Wayyt Wang, name: Bo Zhou, name: Di Wang

The growing disparity between the exponential scaling of computational
resources and the finite growth of high-quality text data now constrains
conventional scaling approaches for large language models (LLMs). To address
this challenge, we introduce Reinforcement Learning on Pre-Training data
(RLPT), a new training-time scaling paradigm for optimizing LLMs. In contrast
to prior approaches that scale training primarily through supervised learning,
RLPT enables the policy to autonomously explore meaningful trajectories to
learn from pre-training data and improve its capability through reinforcement
learning (RL). While existing RL strategies such as reinforcement learning from
human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR)
rely on human annotation for reward construction, RLPT eliminates this
dependency by deriving reward signals directly from pre-training data.
Specifically, it adopts a next-segment reasoning objective, rewarding the
policy for accurately predicting subsequent text segments conditioned on the
preceding context. This formulation allows RL to be scaled on pre-training
data, encouraging the exploration of richer trajectories across broader
contexts and thereby fostering more generalizable reasoning skills. Extensive
experiments on both general-domain and mathematical reasoning benchmarks across
multiple models validate the effectiveness of RLPT. For example, when applied
to Qwen3-4B-Base, RLPT yields absolute improvements of 3.0, 5.1, 8.1,
6.0, 6.6, and 5.3 on MMLU, MMLU-Pro, GPQA-Diamond, KOR-Bench, AIME24, and
AIME25, respectively. The results further demonstrate favorable scaling
behavior, suggesting strong potential for continued gains with more compute. In
addition, RLPT provides a solid foundation, extending the reasoning boundaries
of LLMs and enhancing RLVR performance.