Research
Turn-Level Best-of-N Sampling Boosts LLM Agent Performance
This paper introduces turn-level best-of-N sampling, a simple inference-time scaling method for LLM agents on long-horizon tasks. Using a generator-selector framework with two off-the-shelf open-weight LLMs, the approach improves accuracy by 5-10% and consistency across repeated attempts. Analysis shows agents adopt desirable behaviors like fewer attempts, better instruction following, and improved debugging.
LLM agentsinference-time scalingsampling methods