Swe-Rl

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swe-rl is an open-source codebase that advances LLM reasoning for software engineering via reinforcement learning. It provides tools for prompt templating, reward function implementation, and an Agentless Mini for fast async inference and code repair.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is swe-rl?

SWE-RL is an official codebase for "Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution," designed to scale reinforcement learning-based LLM reasoning for real-world software engineering tasks. It leverages open-source software evolution data and rule-based rewards to improve LLM performance. The codebase includes prompt templates and a flexible reward function API that supports various editing formats, including sequence similarity for search/replace changes and unified diffs. Additionally, SWE-RL features an Agentless Mini component for fast asynchronous inference, code refactoring, file-level localization, and repair, supporting OpenAI-compatible endpoints and Hugging Face models like Llama-3.3-70B-Instruct.

Best used for

Ideal for developers and researchers who need to advance LLM reasoning in software engineering, automate code repair, and implement reinforcement learning techniques. Especially valuable for those working with open-source software evolution data and seeking flexible reward function implementations for code changes.

Common actions

improve LLM reasoning

automate code repair

implement reinforcement learning

research software evolution

collaborationautomated workflowlow-code/no-codeopen-sourceworkflowsdeepfakegithub copilot"AI Agents"face swapping

Capabilities

Key features

Reinforcement learning for LLMs
Open software evolution data
Rule-based rewards
Prompt templates
Reward function API
Agentless Mini for inference
Code repair and localization

Target Audience

developerprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of software evolution data does SWE-RL leverage?

SWE-RL leverages open-source software evolution data to train and improve LLM reasoning. This data helps the reinforcement learning process by providing real-world examples of code changes and their outcomes, enabling the LLM to learn from practical software development scenarios.

How does Agentless Mini improve code repair?

Agentless Mini enhances code repair through fast asynchronous inference and file-level localization. It supports OpenAI-compatible endpoints and Hugging Face models, allowing for efficient processing and repair of code by focusing on affected files and utilizing multiple reproduction tests for reranking potential solutions.

Can SWE-RL's reward function be customized for different editing formats?

Yes, SWE-RL provides a general reward function API (`calculate_reward`) that can be paired with any editing format. While it offers specialized functions for search/replace changes and unified diffs, the general API allows users to define and compute rewards for various types of code modifications.

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