MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents
This work addresses the challenge of enhancing productivity in machine learning research for researchers, though it appears incremental as it builds on existing autonomous research concepts.
The authors tackled the problem of automating machine learning research by developing MLR-Copilot, a framework using large language model agents to generate and implement research ideas, which showed potential in facilitating ML research progress across five tasks.
Autonomous machine learning research has gained significant attention recently. We present MLR-COPILOT, an autonomous Machine Learning Research framework powered by large language model agents. The system is designed to enhance ML research productivity through automatic generation and implementation of research ideas within constraints. Our work was released in August 2024 (concurrent to AI-Scientist) and has gained notable recognition from leading projects. We further enhance our ideation with training afterwards. The framework consists of three stages: idea generation, experiment implementation, and code execution. First, existing research papers are used to generate feasible ideas and experiment plans with IdeaAgent, powered by an RL-tuned LLM. Next, ExperimentAgent leverages retrieved prototype code to convert plans into executable code with optionally retrieved candidate models and data from HuggingFace. In the final stage, ExperimentAgent runs experiments, and allows subsequent iterations of debugging and human feedback for a better chance of success with executable outcomes. We evaluate our framework on five machine learning research tasks. Experiment results demonstrate the potential of our framework to facilitate ML research progress and innovation.