On Turn-Level Best-of-N Sampling for Scaling LLM Agents
In this work, we evaluate a simple inference-time scaling approach based on \textit{turn-level best-of-N sampling} (\method) for large language model (LLM) agents on complex, long-horizon tasks. We instantiate this using a \textit{generator-selector} framework with two LLMs; at each turn, a \emph{generator} model generates $N$ candidate actions and a \emph{selector} model selects one of them,…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
