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.
Techub News reported that AI startup Sakana AI has developed a self-improving model that uses the MASS method to let a single language model improve itself by solving tasks, evaluating results, and optimizing its workflow.
After two rounds of refinement, the model’s win rate in benchmark tests rose to nearly 70% compared with the base model.
The research also identified a weakness. The model showed blind spots on some errors, and if it makes the same mistake across all task roles, the training process may misread that as progress and reinforce it.
The original source cited for the report was The Decoder.
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