Study argues exact enumeration outperforms RL for genomic tool selection

A new arXiv paper questions the widespread practice of training a policy with reinforcement learning on top of a frozen reasoning model to decide which external tools an AI system should call. The authors contend that in specialized scientific domains such as genomics, where the set of possible tool combinations is small enough to list exhaustively, sampling-based methods are unnecessary. They instead present an exact optimization approach that evaluates the full space of tool subsets to recover optimal selection policies.