Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills

• URL: http://arxiv.org/abs/2606.07412v1
• Date: Fri, 05 Jun 2026 16:00:17 GMT
• Title: Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
• Abstract summary: We introduce Socratic-SWE, a closed-loop self-evolution framework that reuses the agent's historical solving traces as a source of training signal.<n>Rather than treating traces only as evidence for reward computation, Socratic-SWE distills them into structured agent skills that summarize recurring failures and effective repair patterns.
• Score: 12.442806027914097
• License: http://creativecommons.org/licenses/by/4.0/
• 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 signal. Rather than treating traces only as evidence for reward computation, Socratic-SWE distills them into structured agent skills that summarize recurring failures and effective repair patterns. These skills then guide the generation of targeted repair tasks in real repositories. Candidate tasks are checked through execution-based validation and scored with a solver-gradient alignment reward, so that the retained tasks are both verifiable and useful for improving the Solver. The updated Solver produces new traces, enabling the task curriculum to adapt over successive rounds. Across SWE-bench Verified, SWE-bench Lite, SWE-bench Pro, and Terminal-Bench 2.0, Socratic-SWE consistently improves over self-evolving baselines under the same compute budget, reaching 50.40% on SWE-bench Verified after three iterations. These results suggest that solving traces can serve as a scalable substrate for self-evolving SWE agents.

Related papers

• TTHE: Test-Time Harness Evolution [50.26245555541721]
  Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time.<n>We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs.<n>During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces.<n>A judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs.
  arXiv  Detail & Related papers  (2026-07-09T05:53:39Z)
• RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents [0.0]
  RigorBench is the first benchmark to measure process discipline in AI coding agents.<n>It evaluates harnesses across five pillars: Planning Fidelity, Verification Coverage, Recovery Efficiency, Abstention Quality, and Atomic Transition Integrity.<n>Our results show that structured process discipline improves process quality scores by an average of 41% and raises downstream outcome correctness by 17%.
  arXiv  Detail & Related papers  (2026-06-21T21:41:34Z)
• GRACE-DS: a Guarded Reward-guided Agent Correction Environment in Data Science [39.146761527401424]
  GRACE-DS is a Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agents.<n>It exposes agents to realistic workflow stages, from planning and data inspection through feature engineering, model development, validation, and code repair to final submission.<n>These results establish GRACE-DS as a robust platform for assessing the capacity of LLM-based AutoML agents to execute machine learning under production-like conditions.
  arXiv  Detail & Related papers  (2026-06-14T19:58:06Z)
• SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents [52.758563996979596]
  Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use.<n>We propose SENTINEL, a failure-driven reinforcement learning framework that turns rollout failures into targeted training tasks.<n>On Tau2-Bench Retail with Qwen3-4B-Thinking-2507, SENTINEL improves Pass1 from 66.4 to 74.9 and outperforms RL on general synthetic tasks across Passk metrics.
  arXiv  Detail & Related papers  (2026-06-11T05:06:50Z)
• Self-evolving LLM agents with in-distribution Optimization [60.05867547965365]
  Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments.<n>We propose Q-Evolve, a self-evolving framework for LLM agents that unifies automatic process-reward labeling and policy learning.<n>We evaluate our method on AlfWorld, WebShop, and ScienceWorld, showing Q-Evolve outperforms strong baselines in sample efficiency, robustness, and overall task performance.
  arXiv  Detail & Related papers  (2026-06-05T15:09:52Z)
• Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents [0.3733676450456031]
  We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning.<n>Rather than training a new model or only sampling more candidate solutions, Trace2Skill treats the agent's natural-language skill as an evolvable policy.<n>It mines repeated rollout traces for success and failure modes, converts them into dense diagnostics and oracle lessons, and uses an oracle, mutator, and selector loop to produce task-specific skills.
  arXiv  Detail & Related papers  (2026-05-20T23:10:49Z)
• SWE-Shepherd: Advancing PRMs for Reinforcing Code Agents [0.31034395048547575]
  Existing approaches rely on static prompting strategies or handcrafteds to select actions such as code editing, file navigation, and test execution.<n>We propose SWE-Shepherd, a framework that introduces Process Reward Models (PRMs) to provide dense, step-level supervision for repository-level code agents.<n>Experiments on SWE-Bench Verified demonstrate improved interaction efficiency and action quality, while also highlighting challenges in aligning intermediate rewards with final task success.
  arXiv  Detail & Related papers  (2026-04-12T06:51:47Z)
• CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning [4.765206163164323]
  CLEANER exploits intrinsic self-correction capabilities to eliminate error-contaminated context during data collection.<n>Similarity-Aware Adaptive Rollback mechanism autonomously constructs clean, purified trajectories.<n>Results show average accuracy gains of 6%, 3%, and 5% over baselines.
  arXiv  Detail & Related papers  (2026-01-21T16:14:30Z)
• AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering [8.201374511929538]
  AgentDevel is a release engineering pipeline that iteratively runs the current agent.<n>It produces implementation-blind, symptom-level quality signals from execution traces.<n>It aggregates dominant symptom patterns and produces auditable engineering specifications.
  arXiv  Detail & Related papers  (2026-01-08T05:49:01Z)
• Toward Training Superintelligent Software Agents through Self-Play SWE-RL [66.11447353341926]
  Self-play SWE-RL is a first step toward training paradigms for superintelligent software agents.<n>Our approach takes minimal data assumptions, only requiring access to sandboxed repositories with source code and installed dependencies.<n>Our results, albeit early, suggest a path where agents autonomously gather extensive learning experiences from real-world software repositories.
  arXiv  Detail & Related papers  (2025-12-21T00:49:40Z)
• BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuning [82.925106913459]
  Reinforcement finetuning (RFT) is a key technique for aligning Large Language Models (LLMs) with human preferences and enhancing reasoning.<n>We introduce BOTS, a unified framework for Bayesian Online Task Selection in RFT reinforcement finetuning.
  arXiv  Detail & Related papers  (2025-10-30T11:15:23Z)
• Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm [60.36837655498119]
  We propose a Trajectory-based validated-by-Reproducing Agent-benchmark Complexity Evolution framework.<n>This framework takes an original task from an existing benchmark and encourages agents to evolve it into a new task with higher difficulty.<n>Experiments on the GAIA benchmark demonstrate that the TRACE framework consistently enhances task complexity while improving the reliability of correctness.
  arXiv  Detail & Related papers  (2025-10-01T01:52:52Z)
• Self-Challenging Language Model Agents [98.62637336505242]
  We propose the Self-Challenging framework for training an agent on high-quality tasks that are generated by itself.<n>The framework achieves over a two-fold improvement in Llama-3.1-8B-Instruct, despite using only self-generated training data.
  arXiv  Detail & Related papers  (2025-06-02T14:23:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.