Inference-Time Search
Overview
Inference-time search studies how models can use additional computation at deployment to explore alternatives and improve their answers or decisions. Common approaches include best-of-N sampling, beam search and Monte Carlo tree search, guided by verifiers or reward models, with adaptive budgets, pruning, and memory used to focus effort. These methods are applied to mathematical reasoning, code and theorem proving, tool-using agents, planning, and scientific design, where stronger selection can matter as much as generating more candidates. A central tension is that search helps only when its guidance is informative: noisy scores can favor flawed paths, while broader exploration and verification cost time and compute.
Latest papers
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