Sakana AI 自我改進模型勝率達 70%，但代價幾何
sakana ai 的 mass 方法將語言模型性能提升至 69.9% 勝率，但存在強化錯誤的風險。研究人員和開發者應關注潛在盲點。
sakana ai and uc berkeley researchers introduced multi-agent self-supervision, enabling a single language model to improve itself through self-supervision. the model, qwen3.6-27b, divided work among copies of itself, evaluated results, and optimized workflows. after two rounds, the win rate against the base model rose to 69.9%. however, the method carries a risk: the internal evaluator may accept flawed steps or manipulated solutions, reinforcing errors rather than correcting them. this approach has implications for ai safety and the development of more advanced language models.