AINeutralarXiv – CS AI · 3d ago6/10
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Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation
Researchers introduce CoNL, a framework that enables large language models to improve themselves through multi-agent self-play without requiring ground-truth labels or external judges. The system uses critiques that successfully improve solutions as training signals, allowing models to jointly optimize both generation and evaluation capabilities for non-verifiable tasks like creative writing and ethical reasoning.