ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

Key points

• Problem: Training ML research agents needs a large amount of process-level data that includes debugging, failed experiments, and step-by-step reasoning. Existing papers, code repositories, and static benchmarks record only final outcomes, and human-made benchmarks have too few tasks to give the scale and diversity that agentic SFT needs.
• Input and output: This is a method paper that combines training-data generation with fine-tuning. The pipeline takes a teacher LLM and the HuggingFace dataset hub and outputs executable ML research environments (task description, dataset, baseline code, evaluation script) plus teacher trajectories. The trained student agent works in MLGym by reading and editing files and running shell commands, and has to submit a solution that improves on the baseline within a fixed interaction budget.
• Method: In phase 1 the teacher samples a topic and proposes a task and dataset name, which is matched to a real dataset through HuggingFace search, and then writes the configuration, starter code, and evaluation script. In phase 2 the task is run once in MLGym, and on errors a self-debugging loop repairs the code from the error log or regenerates it, keeping only environments that run. In phase 3 up to 256 teacher trajectories are collected per task, only those that beat the baseline or pass a Z-score threshold (scores standardized by the task mean and standard deviation) are kept, and trajectories longer than 32,768 tokens are dropped.
• Implementation: The teacher is GPT-5, and the students are Qwen3-4B, Qwen3-8B, Qwen3-14B, and Qwen2.5-3B-Instruct. Of 1,000 sampled topics, 914 passed dataset validation and 555 of those (60.7%) passed environment verification; about 34K trajectories were filtered to 17K for SFT. The paper checklist states that public repository links and full reproduction instructions are not yet provided.
• Results: Averaged over three evaluation seeds (42, 123, 456), MLGym AUP rose by 5.7%, 9.1%, and 5.9% at 4B, 8B, and 14B, and the normalized score on MLAgentBench (a benchmark of ML design and debugging tasks) rose by 0.101, 0.171, and 0.294. With added GPT-5.4 trajectories, the 14B model reached a seed-42 best-run AUP of 1.1297, 7.0% above Qwen3-14B. When only the trajectory-generation teacher was replaced with Qwen3-32B, with task synthesis and verification still on GPT-5, mean AUP still rose by 3.0% at 8B and 3.2% at 14B.
• Limits: Because the training objective is supervised imitation of the teacher, the authors leave direct optimization of exploration and scientific novelty to future work with RL. Task generation and verification still rely on GPT-5, and generating and evaluating long-horizon trajectories needs substantial compute. Results vary by task: the 4B model regressed on MNLI (natural language inference) and the 14B model on Breakout (a MinAtar game task), and 6 synthetic tasks (1.1%) exceed 0.60 cosine similarity to an MLGym task.

Paper links

External research summaries. These are not HDATF publications or measured product results.