9 papers
PreferenceFlow: Test-Time Guidance of Flow-Matching Robot Policies from Human Interventions
Yiqi Tang, Diyuan Shi, Runze Li +1
Flow-matching policies can represent complex robot behaviors but remain susceptible to local errors under distribution shift at deployment. Many reinforcement learning approaches t…
In-Context Learning for Robots: Methods and Applications
Haojian Huang, Zexi Li, Junhao Guo +36
General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this pro…
UI-Venus-2 Technical Report
Venus Team, Zhuohan Cai, Haoxing Chen +28
Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remai…
MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation
Hang Yan, Zhangxuan GU, Beitong Zhou +5
Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, existing agents are typically…
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Zelin He, Haotian Lin, Boran Han +6
Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strate…
StepAudio 2.5 Technical Report
Bin Lin, Bo Zhao, Boyong Wu +98
Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…
World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
Runze Li, Hongyin Zhang, Junxi Jin +5
Vision-Language-Action (VLA) models have emerged as a promising paradigm for building embodied agents that ground perception and language into action. However, most existing approa…
CRL-VLA: Continual Vision-Language-Action Learning
Qixin Zeng, Shuo Zhang, Hongyin Zhang +6
Lifelong learning is critical for embodied agents in open-world environments, where reinforcement learning fine-tuning has emerged as an important paradigm to enable Vision-Languag…
RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models
Hongyin Zhang, Shuo Zhang, Junxi Jin +3
Vision-Language-Action (VLA) models have recently emerged as powerful general-purpose policies for robotic manipulation, benefiting from large-scale multi-modal pre-training. Howev…