做LLM数学推理RL训练的团队，GRPO的KL系数和采样策略可以照搬这个改进，AIME 2025上13个点的提升值得一试。
论文发现GRPO算法存在两个效率问题：固定KL系数限制模型探索，均匀采样忽略中等难度题目的信息价值。提出FG-ExPO方法，包含两个轻量组件：基于准确率的自适应KL缩放（AKL）动态调整约束强度，以及高斯课程采样（GCS）聚焦模型学习前沿。在DeepSeek-R1-Distill-Qwen-1.5B和Qwen3-8B-Base上测试，AIME 2025 pass@32从63.33%提升至76.67%，8B模型平均提升2.66%。该方法在固定推理预算下扩大了模型有效探索空间。
fg-expo: Frontier-guided exploration-prioritized policy optimization via adaptive kl and gaussian curriculum
Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, with Group Relative Policy Optimization (GRPO) serving as the dominant algorithm. We identify two overlooked inefficiencies inherent in GRPO. First, a fixed KL coefficient overly restricts policy exploration at moments when the model needs to diverge significantly from the reference policy. Second, uniform question sampling overlooks that moderately difficult problems produce the most informative gradient signals. We propose FG-ExPO, short for Frontier-Guided Exploration-Prioritized Policy Optimization, which integrates two lightweight components. Accuracy-Conditioned KL Scaling (AKL) adjusts the KL penalty strength through a smooth nonlinear function of batch average accuracy, loosening the constraint when the model performs poorly and strengthening it when the model achieves satisfactory results. Gaussian Curriculum Sampling (GCS) assigns sampling weights to questions following a Gaussian distribution centered at a moderate accuracy level around 0.5, focusing model training on its learning frontier. We conduct evaluations on DeepSeek-R1-Distill-Qwen-1.5B and Qwen3-8B-Base across six mainstream mathematical reasoning benchmarks. Experimental results demonstrate that FG-ExPO consistently outperforms vanilla GRPO. It delivers an absolute improvement of 13.34 on the AIME 2025 pass@32 metric, rising from 63.33 percent to 76.67 percent, and obtains an average pass@32 gain of 2.66 on the 8B model. The substantially larger performance gains observed on pass@32 compared to pass@1 verify that FG-ExPO enlarges the model's effective exploration space under a fixed inference budget.