这篇论文提出一种课程强化学习，能帮LLM突破自己的推理能力边界，在多个模型上效果显著，值得关注。
传统RLVR方法仅重新分配采样概率，虽能提升pass@1但可能降低pass@k，无法扩展基础模型的推理能力边界。本文提出边界感知课程RL：先用pass@k采样定位当前推理边界，再对边界附近样本进行教师引导，最后用RL巩固新推理模式。在Qwen、Llama、DeepSeek等模型上，该方法在pass@256上平均比基础模型提升9.8个百分点，比Vanilla RLVR提升10.3个百分点。实验表明该策略可帮助LLM持续突破经验推理边界。
Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model
Reinforcement learning with verifiable rewards (RLVR) is widely viewed as a promising path toward continuously improving large language models. Recent works, however, suggest that mainstream RLVR often reallocates sampling probabilities among trajectories already present in the base model: it can improve sampling efficiency, reflected by higher pass@1 scores, but yields limited gains, and can even decrease pass@k scores when k is large, and therefore may fail to expand the base model's reasoning capacity boundary. In this paper, we present a boundary-aware Curriculum RL approach to move beyond the base model's reasoning capacity boundary. Our approach first uses pass@k sampling to locate the current reasoning capacity boundary, then applies targeted teacher guidance to examples near or beyond that boundary, and finally uses RL to consolidate the newly introduced reasoning patterns. Across Qwen, Llama, and DeepSeek base models, boundary-aware Curriculum RL improves both pass@1 scores and pass@256 scores, with pass@1 reflecting one-attempt performance and pass@256 serving as an empirical proxy for the reasoning capacity boundary. In our experiments, average pass@256 improves by 9.8 percentage points over the base models and by 10.3 percentage points over Vanilla RLVR. These results suggest that boundary-aware Curriculum RL can provide a scalable route for LLMs to continuously improve beyond the base model's empirical reasoning capacity boundary.