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.

翻译：自主机器学习研究近来受到广泛关注。本文提出MLR-COPILOT，一个由大语言模型代理驱动的自主机器学习研究框架。该系统旨在通过自动生成并在约束条件下实施研究思路，提升机器学习研究效率。我们的工作于2024年8月发布（与AI-Scientist同期），已获得多个领先项目的显著认可。我们进一步通过后续训练增强了思路生成能力。该框架包含三个阶段：思路生成、实验实施与代码执行。首先，利用现有研究论文，通过基于强化学习调优的大语言模型驱动的IdeaAgent生成可行的研究思路与实验计划。接着，ExperimentAgent借助检索到的原型代码，将计划转化为可执行代码，并可选择性地从HuggingFace检索候选模型与数据。在最后阶段，ExperimentAgent运行实验，并允许后续进行调试迭代与人工反馈，以提高获得可执行成果的成功率。我们在五项机器学习研究任务上评估了该框架。实验结果表明，我们的框架具有促进机器学习研究进展与创新的潜力。