Jie Tang

A machine learning framework for ERA5-PWV bias calibration

Kai Li, Yun Xie, Jie Tang, De-rui Luo, Junjie Xu, Hanquan Cheng, et al.

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

Zhenyu Hou, Yujiang Li, Jie Tang, Yuxiao Dong

Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged…Read more

CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents

Yujiang Li, Zhenyu Hou, Yi Jing, Jie Tang, Yuxiao Dong

Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed. Context compaction offers a natural solution by summarizing previous interaction states and continu…Read more

SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies

Dijia Zhan, Xuemiao Xu, Jinyi Li, Jie Tang

Vision-Language-Action (VLA) policies that execute fixed-length action chunks can exhibit multimodal bifurcation: a cross-chunk inconsistency in which adjacent chunks generated from independent Gaussian latents can converge to incompatible trajectory modes, producing abrupt disco…Read more