Yu-Jie Zhang

It is proved that symmetric losses enable successful policy improvement even with noisy labels, as the resulting reward is rank-preserving—a property that is identified as sufficient for policy improvement.

OrthoPilot outperformed seasoned orthopaedic experts in diagnostic reasoning, achieving a 10.6% increase in management success for complex musculoskeletal cases.

ACE achieves a remarkable 70% success rate in constraint retrieval tasks without any task-specific retraining, showcasing the power of zero-shot workflow reasoning in robotic manipulation.

XS-VLA outperforms larger models by leveraging spatial distillation and generative flow control, achieving remarkable efficiency in robotic manipulation.

By integrating frequent directions matrix sketching, EOFD-MLogB slashes the computational costs of multinomial logistic bandits without sacrificing performance.

SkeMex enables medical agents to evolve their reasoning capabilities by transforming raw experience into structured, reusable skills, outperforming traditional memory systems.

Even with noisy human preferences, symmetric losses can guarantee rank-preserving rewards, unlocking robust policy optimization for aligning language models.