Sakana AI says self-improving model neared a 70% win rate after two refinement rounds

Sakana AI, an AI startup, said its self-improving model uses the MASS method to let a single language model improve itself by solving tasks, judging outputs, and refining its workflow. According to the report cited by Techub News, the model’s win rate in benchmark testing rose to nearly 70% versus the base model after two rounds of improvement. The research also flagged a limitation. In some cases, the model showed blind spots around certain errors. If the same mistake appears across all task roles handled by the model, the training process may treat that error as progress and reinforce it instead of correcting it. The original source for the item was The Decoder.