According to a paper published on arxiv.org, researchers have introduced ML-Agent, a novel framework for training large language model (LLM)-based agents to autonomously perform machine learning engineering tasks using online reinforcement learning (RL).
The research addresses limitations in current prompt-based approaches, where smaller models struggle to learn from execution trajectories while large proprietary models require high computational overhead. According to the paper, this represents “the first time” exploring “the paradigm of learning-based agentic ML, where an LLM agent learns through interactive experimentation on ML tasks using online reinforcement learning.”
The framework includes three key components: exploration-enriched fine-tuning for diverse action generation, step-wise RL for efficient training on individual action steps, and an ML-specific reward module that unifies various feedback signals into consistent rewards for optimization.
According to the researchers, ML-Agent is powered by a 7B-sized Qwen-2.5 LLM and was trained on only 9 ML tasks. Despite its smaller size, the paper claims it “achieves comparable performance to agents using much larger proprietary LLMs (e.g., GPT-5) but at significantly lower computational cost,” while demonstrating “strong performance and cross-task generalization.”
The research was published on May 4, 2026, and focuses on advancing autonomous machine learning engineering through reinforcement learning rather than traditional prompting approaches.