Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents

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arXiv:2610.08993v1 Announce Type: new Abstract: As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation, limiting the speed and scale of agent evolution. Yet verification efficiency remains under-explored, and frontier models provide only limited gains when used directly as idea sel

Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents

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