AI Scientist Via Synthetic Task Scaling

With the advent of AI agents, automatic scientific discovery has become a tenable goal. Many recent works scaffold agentic systems that can perform machine learning research, but don’t offer a principled way to train such agents—and current LLMs often generate plausible-looking but ineffective ideas. To make progress on training agents that can learn from doing, we provide a novel synthetic environment generation pipeline targeting machine learning agents. Our pipeline automatically synthesizes machine learning challenges compatible with the SWE-agent Yang et al. (2024) framework, covering topic sampling, dataset proposal, and code generation. The resulting synthetic tasks are 1) grounded in real machine learning datasets, because the proposed datasets are verified again the Huggingface API and are 2) verified for higher quality with a self-debugging loop. To validate the effectiveness of our synthetic tasks, we tackle MLGym (Nathani et al. (2025)), a benchmark for machine learning tasks. From the synthetic tasks, we sample trajectories from a teacher model (GPT-5 Singh et al. (2024)), then use the trajectories to train a student model (Qwen3-4B and Qwen3-8B (Yang et al. (2025a))). The student models trained with our synthetic tasks achieve improved performance on MLGym rasing the AUP metric by 9% for Qwen3-4B and and 12% for Qwen3-8B.