Minimal Sufficient Skills: Information-Bottleneck Evolution of Procedural Knowledge

Large language model agents use reusable skills to encode procedural knowledge for specialized, multi-step tasks. Existing skill-evolution methods refine these artifacts using execution feedback but focus primarily on adding or revising content, without identifying which components are necessary for transferable behavior. Evolved skills can therefore accumulate obsolete rules, redundant procedures, instance-specific details, and verifier-dependent content. Conventional length penalties and free-form summarization cannot distinguish dispensable tokens from behavior-critical instructions. We introduce MSS, a framework for identifying minimal sufficient procedural knowledge under explicit behavioral constraints. MSS formulates skill evolution as a multi-objective search that jointly considers task reward, description length, dependence on construction instances, and preservation of transferable behavior. Its deletion-first procedure removes candidate modules, merges equivalent rules, abstracts examples into task-level invariants, and eliminates verifier-specific content before adding new information. Module-level counterfactual tests accept a reduction only when the targeted behavior remains supported under the prescribed validation conditions. This process jointly compresses textual instructions and executable utilities. The resulting Pareto frontier characterizes the trade-off between compactness and behavioral sufficiency without imposing a single unconstrained skill-selection criterion. We analyze this frontier using skill information density, behavior-per-token, and compression robustness, and define an evaluation protocol covering in-distribution performance, cross-task transfer, cross-model reuse, and robustness to tool perturbations. This formulation treats skill compression as behavior-preserving procedural reduction beyond surface-level shortening.