self-supervision

Researchers from Sakana AI and UC Berkeley introduced Multi-Agent Self-Supervision (MASS), in which a single model solves tasks, judges results and optimizes workflows. Applied to Qwen3.6-27B, the win rate against the base model reached 69.9% after two rounds. But the self-evaluator sometimes accepted flawed or manipulated solutions as successes and fed them into the next training cycle.