Research
When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models
arXiv:2608.30395v1 Announce Type: new Abstract: As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inf
arXiv:2608.30395v1 Announce Type: new 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.
Related
- Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling
- LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification
- ReTreVal: Reasoning Tree with Validation and Cross-Problem Memory for Large Language Models
- Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs
Source: arXiv cs.CL | 2026-09-01