Аннотация
Modern code agents achieve strong results on challenging software engineering benchmarks such as SWE-bench, but solving each issue remains expensive: most inference cost is spent not on patch generation, but on repository exploration and search, accounting for up to 56% of tokens in our experiments. We propose SAGE, a modular approach that trains a small searcher model to handle codebase exploration as a tool for a frozen large solver model. We first distill the search trajectories from a strong agent to obtain a compact searcher with comparable retrieval quality. We then apply reinforcement learning to optimize the searcher for usefulness under the solver's fixed interface, using step-level feedback that directly evaluates whether retrieved context is actionable for downstream patching. On SWE-bench Verified, SAGE improves resolve rate while reducing search overhead by 67% and overall cost by 21%, demonstrating a practical path to cheaper, modular software engineering agents.