Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
📰 ArXiv cs.AI
Learn how Harness-Aware Self-Evolving (HASE) co-evolves model weights, harness, and task solutions using agentic reinforcement learning, improving text-classification performance
Action Steps
- Implement HASE using a Qwen3-8B model to co-evolve model weights and harness components
- Edit selected harness components in a multi-turn action space to adapt to changing task requirements
- Evaluate the performance of HASE against traditional self-evolving frameworks and larger models like GPT-OSS-120B
- Apply HASE to text-classification tasks to achieve improved performance and efficiency
- Analyze the impact of HASE on model interpretability and explainability
Who Needs to Know This
ML researchers and engineers can benefit from HASE to improve model performance and adaptability, while software engineers can apply HASE to develop more efficient and dynamic systems
Key Insight
💡 HASE enables a single model to generate task solutions and edit harness components, outperforming larger models in text classification
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🚀 Harness-Aware Self-Evolving (HASE) boosts text-classification performance with agentic reinforcement learning! 🤖
Key Takeaways
Learn how Harness-Aware Self-Evolving (HASE) co-evolves model weights, harness, and task solutions using agentic reinforcement learning, improving text-classification performance
Full Article
Title: Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
Abstract:
arXiv:2607.03935v1 Announce Type: new 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
Abstract:
arXiv:2607.03935v1 Announce Type: new 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
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