When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models

Abstract:As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inference-time compute to a fixed model prior. Viewed at a high level, TTS reframes inference as search over a space of partial reasoning states. While Chain-of-Thought (CoT) exposes intermediate steps, common instantiations rely on single-trajectory decoding, limiting recovery from early errors and exploration. This survey systematizes recent progress in tree-search-based reasoning, viewing inference as instance-specific optimization rather than decoding. We trace the evolution from uninformed search to Monte Carlo Tree Search (MCTS), highlighting how sampling-based control supports principled exploration-exploitation trade-offs. To unify a fragmented literature, we introduce a Unified Design Space spanning search topology, evaluation signals, and control dynamics, and advocate a standardized compute-reporting abstraction to make compute-accuracy trade-offs explicit and comparable.

Computation and Language (cs.CL)

arXiv:2608.30395 [cs.CL]

(or arXiv:2608.30395v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.30395 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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