Scientific experimentation involves an iterative process of creating hypotheses, designing experiments, running experiments, and analyzing the results. Can we build AI research agents to perform these long-horizon tasks? To take a step towards building and evaluating research agents on such open-ended decision-making tasks, we focus on the problem of machine learning engineering: given a task description and a dataset, build a high-performing model. In this paper, we propose MLAgentBench, a suite of ML tasks for benchmarking AI research agents. Agents can perform actions like reading/writing files, executing code, and inspecting outputs. With these actions, agents could run experiments, analyze the results, and modify the code of entire machine learning pipelines, such as data processing, architecture, training processes, etc. The benchmark then automatically evaluates the agent's performance objectively over various metrics related to performance and efficiency. We also design an LLM-based research agent to automatically perform experimentation loops in such an environment. Empirically, we find that a GPT-4-based research agent can feasibly build compelling ML models over many tasks in MLAgentBench, displaying highly interpretable plans and actions. However, the success rates vary considerably; they span from almost 90\% on well-established older datasets to as low as 10\% on recent Kaggle Challenges -- unavailable during the LLM model's pretraining -- and even 0\% on newer research challenges like BabyLM. Finally, we identify several key challenges for LLM-based research agents such as long-term planning and hallucination. Our code is released at https://github.com/snap-stanford/MLAgentBench.

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