@inproceedings{tan-etal-2026-scaling,
    title = "Scaling Behaviors of {LLM} Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning",
    author = "Tan, Zelin  and
      Geng, Hejia  and
      Yu, Xiaohang  and
      Zhang, Mulei  and
      Wan, Guancheng  and
      Zhou, Yifan  and
      He, Qiang  and
      Xue, Xiangyuan  and
      Zhou, Heng  and
      Fan, Yutao  and
      Li, Zhong-Zhi  and
      Zhang, Zaibin  and
      Zhang, Guibin  and
      Zhang, Chen  and
      Yin, Zhenfei  and
      Torr, Philip  and
      Bai, Lei",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1444/",
    pages = "31300--31319",
    ISBN = "979-8-89176-390-6",
    abstract = "While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post-training remains largely unexplored. This paper investigates the scaling behavior of Large Language Model (LLM) reinforcement learning post-training, focusing on mathematical reasoning. Through experiments across the Qwen2.5 series (0.5B to 72B), we characterize how model scale, data, and compute interact. Our analysis yields four key findings: 1. Larger models consistently demonstrate superior compute and data efficiency. 2. The relationship between model performance and training resources follows a **predictive power-law** across both base and instruction-tuned models. 3. RL learning efficiency exhibits a latent **saturation trend** with increasing model scale. 4. In data-constrained regimes, performance is primarily driven by the **total volume of training data** rather than sample uniqueness. These results offer practical guidelines for scaling reasoning capabilities through reinforcement learning post-training."
}