Hong Yan

As large language model (LLM) agents advance beyond software engineering (SWE) toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more expensive: while SWE tasks can be verified via fast-executing unit tests, MLE verification requires running full ML pipelines---data preprocessing, model training, and metric evaluation---on large datasets at each rollout step, rendering trajectory-wise on-policy reinforcement learning (RL) prohibitively slow. Existing approaches retreat to supervised fine-tuning (SFT) or offline proxy rewards, sacrificing the exploration and generalization benefits of on-policy RL. We argue that sandbox data size is one of the primary sources of this bottleneck. Based on this insight, we introduce SandMLE, a framework that generates diverse, verifiable synthetic MLE environments from seed tasks, preserving the structural and mathematical complexity of real-world problems while constraining datasets to micro-scale (50–200 samples). SandMLE reduces execution time by over 13×, enabling large-scale, on-policy trajectory-wise RL for the first time in the MLE domain. On MLE-bench-lite, SandMLE yields a 20.3\% to 66.9\% relative improvement in medal rate over the SFT baselines across the Qwen3-8B, 14B, and 30B-A3B. Furthermore, the trained policy generalizes across unseen agentic scaffolds, achieving up to 32.4\% relative improvement in HumanRank score on MLE-Dojo.

Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuances of user behavior. However, current state-of-the-art architectures operate under a limiting analogy: they treat use...