Year: 2026
Type: conference-paper
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.
Institutions: Amazon (United States), University of Michigan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7140113567