5 papers
Agent Learning via Early Experience
Kai Zhang, Xiangchao Chen, Bo Liu +27
A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents fro…
Knowledge Transfer Scaling Laws for 3D Medical Imaging
Ho Hin Lee, Dongna Du, Chu Wang +4
Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, where unified pretraining across different imaging modalities (i.e. CT,…
Synthetic Sandbox for Training Machine Learning Engineering Agents
Yuhang Zhou, Lizhu Zhang, Yifan Wu +4
As large language model agents advance beyond software engineering (SWE) tasks toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more…
Scaling Agent Learning via Experience Synthesis
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15
While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts,…
A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis
Yue Yang, Mona Gandhi, Yufei Wang +5
While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge…