Researchers have introduced Harness-Aware Self-Evolving (HASE), a novel agentic reinforcement-learning framework that allows a single model to generate task solutions and simultaneously edit its surrounding harness components. This unified approach demonstrated that a Qwen3-8B model using HASE could achieve performance comparable to a larger GPT-OSS-120B model with Claude Code as its harness proposer in text classification tasks. Furthermore, HASE showed superior results in alpha factor mining and successfully converged to state-of-the-art performance in circle-packing algorithm discovery by repairing imperfect evaluation components. AI
IMPACT Introduces a new framework for agentic reinforcement learning that improves both model solutions and harness components simultaneously.
RANK_REASON The cluster contains a research paper detailing a new AI framework and its performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]
- alpha factor mining
- arXiv
- circle-packing algorithm discovery
- Claude Code
- GPT-OSS-120B
- Harness-Aware Self-Evolving
- HASE
- Qwen3_8B
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