📝 SummarXiv

Abstract

Test-time scaling enables large language models (LLMs) to improve performance on long-horizon reasoning tasks by allocating additional compute at inference. Tree-search-based approaches achieve state-of-the-art results in this setting, but they are notoriously inefficient, often an order of magnitude slower than simpler iterative methods. We introduce Chain-in-Tree (CiT), a plug-in framework that adaptively decides when to branch during search rather than branching at every step. CiT relies on lightweight Branching Necessity (BN) evaluation methods: BN-DP (Direct Prompting), where an auxiliary LLM directly judges whether a step requires branching, and BN-SC (Self-Consistency), which clusters multiple candidate actions to estimate agreement. We integrate CiT into three representative LLM-in-the-loop tree search frameworks: Tree of Thoughts (ToT-BS), ReST-MCTS, and RAP, and evaluate across GSM8K and Math500. Our results show that: (1) BN-DP consistently reduces token generation, model invocations, and runtime by 75-85 percent across all settings, with negligible accuracy loss and sometimes accuracy gains; (2) BN-SC typically yields substantial savings (up to 80 percent) but shows instability in 1-4 out of 14 settings, caused by a small subset of examples that produce very long reasoning steps; (3) the quality of auxiliary LLMs is critical, not only the BN evaluator in BN-DP, but also the models used in BN-SC for clustering and equivalence checking. When these roles are filled by smaller LLMs, performance degrades. Importantly, BN-SC does not require LLMs in domains with deterministic action spaces, where clustering can be done programmatically. We also provide a theoretical guarantee that BN-DP never increases LLM invocations relative to the baseline and release a unified implementation of CiT across ToT-BS, ReST-MCTS, and RAP to facilitate reproducibility and extension.