Sakana AI's Self-Improving Model Boosts Task Performance but Cannot Detect Its Own Blind Spots

Sakana AI's MASS method allows a language model to improve itself by solving, judging, and refining tasks, reaching a nearly 70 percent win rate against its base version after two rounds. However, when the model consistently misses the same errors across all roles, it can mistake them for progress and reinforce them during training.