Kang Liu

Simply adding more multimodal environments can hinder agent performance, but targeted diversity and structured difficulty can transform training outcomes.

SFT leads to task conflicts that can cripple multi-task learning, while RL's variance-limited updates enable seamless task coexistence.

Suboptimal trajectories can amplify errors in long-horizon planning, revealing critical insights into the limitations of current training methods.

MemTools transforms agent memory research by enabling seamless integration and evaluation of diverse memory types across architectures.

AREX achieves superior performance in deep research tasks by recursively refining answers through a novel self-improvement mechanism that outpaces traditional search methods.

Models fine-tuned with LongCrafter data achieve unprecedented performance on long-context tasks, particularly in high-difficulty scenarios.