arXiv:2606.07412v1 Announce Type: cross Abstract: LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-quality SWE tasks. Existing synthetic data methods typically create tasks through fixed mutation or bug-injection procedures, making the resulting distributions largely independent of the agent's own weaknesses and training progress. We introduce Socratic-SWE, a closed-loop self-evolution framework that reuses the agent's historical solving traces as a source of training si
The increasing sophistication of LLMs is driving research into more autonomous and self-improving agent systems to tackle complex tasks like software engineering.
This development indicates a significant step towards AI agents that can learn and adapt their skills, potentially accelerating software development and reducing human intervention.
AI software engineering agents will become more self-sufficient, moving away from reliance on manually curated training data to leveraging their own execution traces for improvement.
- · AI software development platforms
- · Large language model developers
- · Software engineering teams
- · Companies seeking automated workflow solutions
- · Monotonous coding task providers
- · Traditional synthetic data generation methods
Socratic-SWE enables AI agents to generate their own high-quality training data by learning from successful and failed attempts.
This self-evolutionary capability will lead to increasingly capable and specialized AI software engineers that can handle complex projects autonomously.
The acceleration of software development cycles could lead to a rapid proliferation of new applications and services, fundamentally reshaping industries.
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Read at arXiv cs.AI