@inproceedings{zhou-etal-2026-lepo,
    title = "{LEPO}: Latent Reasoning Policy Optimization for Large Language Models",
    author = "Zhou, Yuyan  and
      Yu, Jiarui  and
      Dong, Hande  and
      Hao, Zhezheng  and
      Wang, Hong  and
      Zhang, Jianqing  and
      Lin, Qiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.707/",
    pages = "14416--14427",
    ISBN = "979-8-89176-395-1",
    abstract = "Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space.However, without stochastic sampling, these methods inevitably collapse to deterministic inference, failing to discover diverse reasoning paths.To bridge the gap, we inject controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL).Building on this, we propose **L**atent R**e**asoning **P**olicy **O**ptimization (**LEPO**), a novel framework that applies RL directly to continuous latent representations.Specifically, in rollout stage, LEPO maintains stochasticity to enable diverse trajectory sampling, while in optimization stage, LEPO constructs a unified gradient estimation for both latent representations and discrete tokens."
}