Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
📰 ArXiv cs.AI
Learn how Socratic-SWE enables self-evolving coding agents to improve their skills through trace-derived agent skills, enhancing real-world language-model capability in software engineering
Action Steps
- Build a closed-loop system using Socratic-SWE to generate tasks based on an agent's weaknesses
- Run the system to collect traces of the agent's performance
- Configure the system to derive agent skills from the collected traces
- Test the effectiveness of the self-evolving coding agents in real-world software engineering tasks
- Apply the insights from Socratic-SWE to improve the training of LLM-driven software engineering agents
Who Needs to Know This
Software engineers and AI researchers on a team can benefit from Socratic-SWE as it improves the training of LLM-driven software engineering agents, allowing for more efficient and effective coding tasks
Key Insight
💡 Socratic-SWE enables coding agents to learn from their own weaknesses, improving their skills and real-world language-model capability
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🤖 Socratic-SWE: Self-evolving coding agents via trace-derived agent skills 🚀
Key Takeaways
Learn how Socratic-SWE enables self-evolving coding agents to improve their skills through trace-derived agent skills, enhancing real-world language-model capability in software engineering
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