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arXiv · 2601.17596

Learning to Ideate for Machine Learning Engineering Agents

Yunxiang Zhang·Kang Zhou·Zhichao Xu·Kiran Ramnath·Yun Zhou·Sangmin Woo·Haibo Ding·Lin Lee Cheong
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Abstract

Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dual-agent framework that separates ideation from implementation. In our system, an implementation agent can request strategic help from a dedicated Ideator. We show this approach is effective in two ways. First, in a training-free setup, our framework significantly outperforms implementation-only agent baselines on MLE-Bench. Second, we demonstrate that the Ideator can be trained with reinforcement learning (RL) to generate more effective ideas. With only 1K training samples from 10 MLE tasks, our RL-trained Qwen3-8B Ideator achieves an 11.5% relative improvement compared to its untrained counterpart and surpasses Claude Sonnet 3.5. These results highlights a promising path toward training strategic AI systems for scientific discovery.

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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong. 2026-01-24. Learning to Ideate for Machine Learning Engineering Agents. https://arxiv.org/abs/2601.17596

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