AI RESEARCH
Breaking the Reward Barrier: Accelerating Tree-of-Thought Reasoning via Speculative Exploration
arXiv CS.LG
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ArXi:2605.10195v1 Announce Type: new Tree-of-Thought (ToT) reasoning structures Large Language Model (LLM) inference as a tree-based search, nstrating strong potential for solving complex mathematical and programming tasks. However, its efficiency is constrained by the reward dependency barrier -- a synchronization bottleneck caused by sequential reward-guided exploration that limits search parallelism and To enhance ToT reasoning efficiency, we observe that the reasoning paths can be explored speculatively to break the reward synchronization barrier.