MASS: Recursive Self-Improvement through Multi-Agent Self-Supervision

Sakana AI proposes the MASS method, having the same model serve as task executor, workflow optimizer, and evaluator simultaneously, using multi-agent self-supervision to provide training signals for recursive self-improvement. The inner loop uses evaluator feedback to search for better team workflows; the outer loop runs selected workflows with multiple random seeds, then fine-tunes the top-ranked trajectories back into shared model weights via LoRA. Starting from Qwen3.6-27B and running two rounds, external judge win rate against the base model on three held-out synthetic research tasks increased from 53.9% to 69.9%; on MLR-Bench, DSBench, ScienceAgentBench, and AstaBench, score per output token reached 1.2–1.6 times the base, but on Terminal-Bench 2.0 and SWE-bench Verified it was 0.96 times and 0.94 times, respectively.