# RLCoder: Reinforcement Learning for Repository-Level Code Completion

arXiv: https://arxiv.org/abs/2407.19487 · HTML: https://mlsanity.com/paper/2407.19487/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2024.

## Built on by (most cited first, 30 of 44)

- [A Survey of Reinforcement Learning for Large Reasoning Models](https://mlsanity.com/paper/2509.08827/) (2025)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [Agents in Software Engineering: Survey, Landscape, and Vision](https://mlsanity.com/paper/2409.09030/) (2024)
- [FLEX: Continuous Agent Evolution via Forward Learning from Experience](https://mlsanity.com/paper/2511.06449/) (2025)
- [Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks](https://mlsanity.com/paper/2602.22817/) (2026)
- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code](https://mlsanity.com/paper/2409.15154/) (2024)
- [Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks](https://mlsanity.com/paper/2505.16901/) (2025)
- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://mlsanity.com/paper/2510.04905/) (2025)
- [Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges](https://mlsanity.com/paper/2504.20799/) (2025)
- [RAMBO: Enhancing RAG-based Repository-Level Method Body Completion](https://mlsanity.com/paper/2409.15204/) (2024)
- [Statically Contextualizing Large Language Models with Typed Holes](https://mlsanity.com/paper/2409.00921/) (2024)
- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
- [RepoGenReflex: Enhancing Repository-Level Code Completion with Verbal Reinforcement and Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.13122/) (2024)
- [Hybrid-Gym: Training Coding Agents to Generalize Across Tasks](https://mlsanity.com/paper/2602.16819/) (2026)
- [Enhancing Project-Specific Code Completion by Inferring Internal API Information](https://mlsanity.com/paper/2507.20888/) (2025)
- [From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs](https://mlsanity.com/paper/2507.22716/) (2025)
- [EffiReasonTrans: RL-Optimized Reasoning for Code Translation](https://mlsanity.com/paper/2510.18863/) (2025)
- [RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models](https://mlsanity.com/paper/2607.25996/) (2026)
- [SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows](https://mlsanity.com/paper/2506.07313/) (2025)
- [CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning](https://mlsanity.com/paper/2507.17548/) (2025)
- [SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling](https://mlsanity.com/paper/2505.24179/) (2025)
- [ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation](https://mlsanity.com/paper/2601.09703/) (2026)
- [DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://mlsanity.com/paper/2601.20615/) (2026)
- [Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents](https://mlsanity.com/paper/2604.14004/) (2026)
- [FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification](https://mlsanity.com/paper/2502.17139/) (2025)
- [Toward Rich Video Human-Motion2D Generation](https://mlsanity.com/paper/2506.14428/) (2025)
- [TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation](https://mlsanity.com/paper/2509.24148/) (2025)
- [Credit-Budgeted ICPC-Style Coding: When Agents Must Pay for Every Decision](https://mlsanity.com/paper/2604.10182/) (2026)
- [ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization](https://mlsanity.com/paper/2605.03117/) (2026)

## Abstract (arXiv)

Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrieval-augmented generation strategies due to limitations in input sequence length. However, traditional lexical-based retrieval methods like BM25 struggle to capture code semantics, while model-based retrieval methods face challenges due to the lack of labeled data for training. Therefore, we propose RLCoder, a novel reinforcement learning framework, which can enable the retriever to learn to retrieve useful content for code completion without the need for labeled data. Specifically, we iteratively evaluate the usefulness of retrieved content based on the perplexity of the target code when provided with the retrieved content as additional context, and provide feedback to update the retriever parameters. This iterative process enables the retriever to learn from its successes and failures, gradually improving its ability to retrieve relevant and high-quality content. Considering that not all situations require information beyond code files and not all retrieved context is helpful for generation, we also introduce a stop signal mechanism, allowing the retriever to decide when to retrieve and which candidates to retain autonomously. Extensive experimental results demonstrate that RLCoder consistently outperforms state-of-the-art methods on CrossCodeEval and RepoEval, achieving 12.2% EM improvement over previous methods. Moreover, experiments show that our framework can generalize across different programming languages and further improve previous methods like RepoCoder. We provide the code and data at https://github.com/DeepSoftwareAnalytics/RLCoder.