AI RESEARCH
Reinforced Efficient Reasoning via Semantically Diverse Exploration
arXiv CS.CL
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ArXi:2601.05053v2 Announce Type: replace-cross Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)-based extensions improve upon vanilla RLVR (e.g., GRPO) by providing tree-based reasoning rollouts that enable fine-grained and segment-level credit assignment. However, existing methods still suffer from limited exploration diversity and inefficient reasoning.