AI RESEARCH
Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories
arXiv CS.AI
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ArXi:2604.11365v1 Announce Type: new Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain inefficient. Standard approaches retain only the single highest-reward trajectory, discarding the comparative signals present in the many explored paths. Here we