# Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR > Source: > Published: 2026-09-10 02:30:39+00:00 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 trai