让AI分工协作，先想策略再执行，提升机器学习优化效率。
Learning to Ideate for Machine Learning Engineering Agents
- 分离创意与执行，由专门代理提出优化策略
- 仅用1000样本训练，效果比未训练版本高11.5%
- 适合研究AI自主科研、自动化机器学习的团队
现有机器学习工程（MLE）代理在迭代优化算法时表现不佳。为此，我们提出MLE-Ideator，一种双代理框架，将创意生成与实现分离。实现代理可向专门的创意代理请求策略支持。实验表明，该方法有效：一是在无训练设置下，显著优于仅实现型代理基线；二是创意代理可通过强化学习训练，生成更优策略。仅需10个任务的1000个训练样本，基于Qwen3-8B的训练后创意代理相比未训练版本实现11.5%相对提升，并超越Claude Sonnet 3.5。结果展示了训练战略性AI系统用于科学发现的潜力。
原文摘要 · Abstract (English)
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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