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arXiv · 2607.03935

Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions

Haochen Luo·Yi Huang·Sichun Luo·Fengyuan Liu·Lei Li·Zefa Hu·Junlan Feng·Qi Liu
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Abstract

Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-learning framework in which a single model can generate task solutions or edit selected harness components in a multi-turn action space. HASE enables a single Qwen3-8B model to match the text-classification performance of a GPT-OSS-120B model that uses Claude Code as the harness proposer. In alpha factor mining, HASE outperforms the reported GPT-OSS-120B baseline. HASE also repairs imperfect evaluation components and converges to state-of-the-art performance in circle-packing algorithm discovery. These results show that HASE improves the harness and the solution through one unified agentic process.

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Haochen Luo, Yi Huang, Sichun Luo, Fengyuan Liu, Lei Li, Zefa Hu, Junlan Feng, Qi Liu. 2026-07-04. Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions. https://arxiv.org/abs/2607.03935

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