1 citations · 5 across the 11 of their papers we have counts for
12 papers
ConvMem: Convolutional Memory for Long-Context Reasoning
Hongming Zhang, Zhaozhen Gu, Fengshuo Bai +6
While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequent…
DexFlyWheel: A Scalable and Self-improving Data Generation Framework for Dexterous Manipulation
Kefei Zhu, Fengshuo Bai, YuanHao Xiang +8
Dexterous manipulation is critical for advancing robot capabilities in real-world applications, yet diverse and high-quality datasets remain scarce. Existing data collection method…
A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
Yifan Zhong, Fengshuo Bai, Shaofei Cai +11
The remarkable advancements of vision and language foundation models in multimodal understanding, reasoning, and generation has sparked growing efforts to extend such intelligence…
PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives
Zhaowei Zhang, Xiaobo Wang, Minghua Yi +5
Achieving political consensus is crucial yet challenging for the effective functioning of social governance. However, although frontier AI systems represented by large language mod…
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
Zhaowei Zhang, Fengshuo Bai, Qizhi Chen +5
How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, chang…
-DQN: Improving Deep Q-Learning By Evolving the Behavior
Hongming Zhang, Fengshuo Bai, Chenjun Xiao +3
While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like -greed…
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
Fengshuo Bai, Runze Liu, Yali Du +2
Evaluating deep reinforcement learning (DRL) agents against targeted behavior attacks is critical for assessing their robustness. These attacks aim to manipulate the victim into sp…
Efficient Model-agnostic Alignment via Bayesian Persuasion
Fengshuo Bai, Mingzhi Wang, Zhaowei Zhang +4
With recent advancements in large language models (LLMs), alignment has emerged as an effective technique for keeping LLMs consensus with human intent. Current methods primarily in…
Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation
Fengshuo Bai, Rui Zhao, Hongming Zhang +5
Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering. However, a notable limitation of PbRL is its depende…
Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
Zhaowei Zhang, Fengshuo Bai, Mingzhi Wang +3
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides h…
ValueDCG: Measuring Comprehensive Human Value Understanding Ability of Language Models
Zhaowei Zhang, Fengshuo Bai, Jun Gao +1
Personal values are a crucial factor behind human decision-making. Considering that Large Language Models (LLMs) have been shown to impact human decisions significantly, it is esse…
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
Runze Liu, Yali Du, Fengshuo Bai +2
In preference-based Reinforcement Learning (RL), obtaining a large number of preference labels are both time-consuming and costly. Furthermore, the queried human preferences cannot…