# Sakana AI and UC Berkeley train a 27B model on its own multi-agent runs, raising its win rate to 69.9% after two rounds

In the method, called Multi-Agent Self-Supervision (MASS), a single Qwen3.6-27B model acts as executor, workflow optimizer and judge without a stronger teacher. The authors say errors that all three roles miss get reinforced as progress.

- Published: 2026-10-13T06:13:53.502Z
- Canonical: https://polylog.news/ai/2026-10-13/sakana-ai-and-uc-berkeley-train-a-27b-model-on-its-own-multi
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Polylog editors](https://polylog.news), [The Decoder](https://the-decoder.com/sakana-ais-self-improving-model-gets-better-at-solving-and-judging-but-cant-catch-its-own-blind-spots/), [AI Front Page](https://aifront-page.com/sakana-ai-mass-recursive-self-improvement/), [Superpowerdaily](https://superpowerdaily.com/posts/sakana-ai-reports-research-gains-from-self-trained-ai-teams-but-coding-efficiency-slips)

Sakana AI, the Tokyo lab, and researchers at UC Berkeley published MASS on arXiv on October 8. The method lets a model improve without human labels or an external teacher model, AI ML Big Data reported. One shared model plays three roles. A…

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