1 citations · 1 across the 8 of their papers we have counts for
9 papers
Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
Xu Luo, Ji Zhang, Lianli Gao +2
Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is bot…
GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
Min Zeng, Jingfei Sun, Xueyou Luo +4
In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tun…
Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation
Youguang Xing, Xu Luo, Junlin Xie +3
Generalist robot policies trained on large-scale datasets such as Open X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks. However, they often struggle…
Multi-Agent Collaboration via Evolving Orchestration
Yufan Dang, Chen Qian, Xueheng Luo +11
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-s…
Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach
Xiaoran Yin, Xu Luo, Hao Wu +2
The automatic control of mobile devices is essential for efficiently performing complex tasks that involve multiple sequential steps. However, these tasks pose significant challeng…
InSpire: Vision-Language-Action Models with Intrinsic Spatial Reasoning
Ji Zhang, Shihan Wu, Xu Luo +4
Leveraging pretrained Vision-Language Models (VLMs) to map language instruction and visual observations to raw low-level actions, Vision-Language-Action models (VLAs) hold great pr…
Policy Contrastive Decoding for Robotic Foundation Models
Shihan Wu, Xu Luo, Ji Zhang +4
Robotic foundation models, or generalist robot policies, hold immense potential to enable flexible, general-purpose and dexterous robotic systems. Despite their advancements, our e…
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
Cheng Chen, Junchen Zhu, Xu Luo +3
Instruction tuning represents a prevalent strategy employed by Multimodal Large Language Models (MLLMs) to align with human instructions and adapt to new tasks. Nevertheless, MLLMs…
From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning
Ji Zhang, Xu Luo, Lianli Gao +3
Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core o…