Modern LLM coding agents—Claude Code, OpenHands—share a common inefficiency: they spend most of their token budget finding the file to patch, not patching it. On SWE-Bench Verified, a 30B OpenHands agent averages 23 rounds and 631K tokens per resolved issue, dominated by grep, glob, and view_file calls. No open, systematically-studied agent-style code retriever exists at scale to shorten this phase. We introduce CodeGrep, a 14B retrieval agent trained end-to-end with GRPO to issue multi-turn parallel grep/glob/read tool calls and return candidate files to a frozen downstream coding agent.

On all 500 SWE-Bench Verified instances, CodeGrep preserves the downstream resolve rate (25.8% → 27.0%, +1.2pp) while delivering a much larger efficiency gain (−15% rounds, −19% tokens on resolved instances). Across three retrievers, downstream utility follows a precision threshold: BM25 (precision 0.375) degrades the agent, Jina (0.445) is neutral, and only CodeGrep (0.677) crosses the threshold at which retrieval begins to buy efficiency. To enable this study, we mine supervision from 67K open-source agent trajectories (CATM) and build a Git-worktree environment for multi-turn agent RL. In our setting, applying the efficiency signal at the advantage layer, rather than the reward layer, holds KL drift to one-third and translates cleanly into downstream efficiency. We will release the model, training pipeline, RL environment, and evaluation harnesses.

Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored. In this work, we propose a novel benchmark, : Code Migration Across Environment, designed to evaluate LLMs’ performance in handling code migration tasks. The benchmark comprises 922 data points across 19 Python and Java packages, offering three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments. Experimental evaluation of across seven LLMs revealed an average pass@1 rate of 26.50%, with GPT-4o performing best at 43.84%. We highlight our key findings as follows: (i) LLMs are more familiar with newer function versions, making them better at migrating legacy code, and (ii) a logical inconsistency where LLMs sometimes identify irrelevant function changes for the target migration environment.