9 papers
Mergeable Model-Side Aggregation States for Long-Context Language Models
Dachuan Song, Junyu Yin, Zechen Hu +1
A known limitation of long-context language models is their increasingly unreliable performance in non-additive, set-based aggregation as context length grows. Examples include car…
Smooth Scaling Laws Hide Stepwise Token Learning
Pingjie Wang, Zechen Hu, Peiru Yang +2
Language model loss follows remarkably regular scaling laws over model and data size, yet it remains unclear why the aggregate loss should exhibit a power-law form. Existing explan…
Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation
Pengxiang Li, Zechen Hu, Zirui Shang +15
Vision-language model (VLM) based GUI agents show promise for automating complex desktop and mobile tasks, but face significant challenges in applying reinforcement learning (RL):…
Robust Online Calibration for UWB-Aided Visual-Inertial Navigation with Bias Correction
Yizhi Zhou, Jie Xu, Jiawei Xia +3
This paper presents a novel robust online calibration framework for Ultra-Wideband (UWB) anchors in UWB-aided Visual-Inertial Navigation Systems (VINS). Accurate anchor positioning…
CARoL: Context-aware Adaptation for Robot Learning
Zechen Hu, Tong Xu, Xuesu Xiao +1
Using Reinforcement Learning (RL) to learn new robotic tasks from scratch is often inefficient. Leveraging prior knowledge has the potential to significantly enhance learning effic…
Learning Coordinated Maneuver in Adversarial Environments
Zechen Hu, Manshi Limbu, Daigo Shishika +2
This paper aims to solve the coordination of a team of robots traversing a route in the presence of adversaries with random positions. Our goal is to minimize the overall cost of t…
Bi-CL: A Reinforcement Learning Framework for Robots Coordination Through Bi-level Optimization
Zechen Hu, Daigo Shishika, Xuesu Xiao +1
In multi-robot systems, achieving coordinated missions remains a significant challenge due to the coupled nature of coordination behaviors and the lack of global information for in…
Scaling Team Coordination on Graphs with Reinforcement Learning
Manshi Limbu, Zechen Hu, Xuan Wang +2
This paper studies Reinforcement Learning (RL) techniques to enable team coordination behaviors in graph environments with support actions among teammates to reduce the costs of tr…
Team Coordination on Graphs with State-Dependent Edge Cost
Sara Oughourli, Manshi Limbu, Zechen Hu +3
This paper studies a team coordination problem in a graph environment. Specifically, we incorporate "support" action which an agent can take to reduce the cost for its teammate to…