Sakana AI 自我改进模型胜率达 70%，但代价几何
sakana ai 的 mass 方法将语言模型性能提升至 69.9% 胜率，但存在强化错误的風險。研究人员和开发者应关注潜在盲点。
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.