**Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasonin

**Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR**

> Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantly improves single-sample accuracy, it often fails to expand the model's intrinsic reasoning coverage (pass@k) due to limited exploration during training. To address this, we optimize the structural design of train-time rollouts to enhance pass@k. Our analysis identifi...

**来源信息**
- **来源**：HuggingFace Daily Papers（社区热门论文）
- **分类**：行业
- **发布时间**：2026-09-10 08:00（北京时间）
- **原文**：[打开原文](https://huggingface.co/papers/2609.08650)