Sakana AI 提出 MASS：用多智能体自监督扩展递归自我改进
作者推荐 Sakana AI 关于递归自我改进的论文，提出多智能体自监督方法 MASS。该方法让一个基础模型提出多智能体工作流、运行并打分，用进化搜索保留得分最高的工作流，再用自身轨迹微调模型，改进后的模型进入下一轮循环，从而不再依赖外部验证器。
Recommended paper from Sakana AI on recursive self-improvement.
They propose an interesting way to scale recursive self-improvement through multi-agent self-supervision.
In this line of research, self-improvement loops usually need an external verifier, so open-ended tasks without a checker are left out.
MASS removes that requirement.
One base model proposes multi-agent workflows, runs them and grades them, and an evolutionary search keeps the workflows that score best. The model is then fine-tuned on its own traces, and the improved model starts the next cycle as a better optimizer and grader.
Two cycles on Qwen3.6-27B raise performance per output token from 1.2 to 1.6x on four open-ended benchmarks.
A student trained on multi-agent traces also beats a single-agent student trained on 1.4x more tokens.
Paper: arxiv.org/abs/2610.12176
Chat with Paper: academy.dair.ai/papers/recur…
来源：omarsar0 · x.com