Stanford's Agent0 evolves agents from zero data, beats self-play baselines
yuyinzhou_cs · x · 2026-10-06
The Stanford paper 'Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning' builds a self-evolving agent framework requiring no human labels, curated tasks, or demonstrations, reportedly outperforming all prior self-play methods. It spawns multiple agents from one base LLM — including a Curriculum Agent that generates tasks — breaking the ceiling where self-improvement methods stall by only generating marginally harder tasks. Presenting at COLM 2026 (Oct 6–9).
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