LEPO: Latent Reasoning Policy Optimization for Large Language Models
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 \textbf{\underline{L}}atent R\textbf{\underline{e}}asoning \textbf{\underline{P}}olicy \textbf{\underline{O}}ptimization~(\textbf{LEPO}), a novel framework that applies RL directly to...
| Authors | Yuyan Zhou, Jiarui Yu, Hande Dong et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 12 June 2026 |
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