expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling
Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, where Group Relative Policy Optimization (GRPO) serves as the mainstream algorithm. We point out two understudied inefficiencies existing in GRPO. First, the fixed KL penalty coefficient overly restricts policy exploration at stages where the model requires significant deviation from the reference policy. Second, uniform sampling of training questions ignores that moderately difficult problems provide the most informative gradient signals for optimization. We propose Exploration-Prioritized Policy Optimization (EXPO) with two lightweight plug-in modules. The Accuracy-Cond...
| Authors | Mingxiong Lin, Zhangquan Gong, Maowen Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 May 2026 |
| Last updated at source | 13 May 2026 |
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