In today’s hottest papers, this one on recursive self-improvement is worth taking apart. The model no longer relies on being fed data by people; instead it creates its own questions, answers them, critiques them, and then teaches the next generation with the improved version. In engineering circles, this kind of self-iteration is called “policy distillation.”
If you put it on-chain, this thread points directly to $FET and $TAO . The former assembles multiple agents into a single team—exactly the kind of setup that needs each agent to be able to iterate on its own. The latter moves the training process onto the chain, letting compute contributors earn rewards according to their contributions. The more the paper proves that self-improvement can actually work, the more concrete the on-chain division of labor becomes.
This paper is, in a sense, another brick added to the path for September.
#递归自改进 #智能体蒸馏 #AI
If you put it on-chain, this thread points directly to $FET and $TAO . The former assembles multiple agents into a single team—exactly the kind of setup that needs each agent to be able to iterate on its own. The latter moves the training process onto the chain, letting compute contributors earn rewards according to their contributions. The more the paper proves that self-improvement can actually work, the more concrete the on-chain division of labor becomes.
This paper is, in a sense, another brick added to the path for September.
#递归自改进 #智能体蒸馏 #AI
