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.

Code edit suggestion, which encompasses modifying, refactoring, and maintaining existing code, represents the most frequent software development activity and has become a focal point for AI-powered tools. Traditional methods translate explicit natural language instructions into code edits, while pattern-based approaches learn from users’ historical editing patterns to provide style-consistent and more accurate suggestions. However, these pattern-based methods still face two critical challenges: (1) difficulty handling edits that demand deep contextual reasoning, and (2) lack of interpretability in editing decisions. To tackle this, we propose CoT-Edit, a reinforcement learning framework that guides LLMs to discover chain-of-thought (CoT) reasoning paths for code editing without requiring human-annotated CoT data. Specifically, we design multi-step reasoning framework that enable: (1) analysis-guided code editing, and (2) seamless switching between CoT and non-CoT inference modes. Building on this, we introduce Edit-Aware Reward Modeling (EARM), a fine-grained diff-based reward approach for effective learning. Furthermore, we discover a LoRA merging strategy that enhances model generalization. Evaluations on an industrial dataset show that our approach achieves 60.2% edit accuracy, outperforming all strong baselines. Online A/B tests further confirm its effectiveness in production. Code is available at https://github.com/202230483077yyh/CoT-Edit.

This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR²) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children’s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.