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

Excerpt

arXiv:2603.17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal. However, training agents to autonomously execute the engineering-heavy labor of machine learning (ML) research requires massive, process-level supervision. Existing static benchmarks omit critical intermediate steps such as debugging and incremental reasoning, and manual data collection is prohibitively expensive. To overcome this data bottl