Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning

Researchers introduce Self-Harmony, a new test-time reinforcement learning framework that improves AI model accuracy by having models solve problems and rephrase questions simultaneously. The method uses harmonic mean aggregation instead of majority voting to select stable answers, achieving state-of-the-art results across 28 of 30 reasoning benchmarks without requiring human supervision.