20 citations · 103 across the 18 of their papers we have counts for
18 papers
DexHoldem: An Agentic Robotics Benchmark for Dexterous Manipulation in Texas Hold'em
Feng Chen, Tianzhe Chu, Li Sun +6
Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing scene (e.g. a tabletop), choose a context-…
Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs
Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong +7
Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental…
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang +6
Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization…
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
Yuexiang Zhai, Hao Bai, Zipeng Lin +8
Large vision-language models (VLMs) fine-tuned on specialized visual instruction-following data have exhibited impressive language reasoning capabilities across various scenarios.…
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement
Ruiqi Zhang, Yuexiang Zhai, Andrea Zanette
What can an agent learn in a stochastic Multi-Armed Bandit (MAB) problem from a dataset that contains just a single sample for each arm? Surprisingly, in this work, we demonstrate…
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
Shengbang Tong, Zhuang Liu, Yuexiang Zhai +3
Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However, the visu…
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
Marwa Abdulhai, Isadora White, Charlie Snell +5
Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed age…
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
Yaodong Yu, Sam Buchanan, Druv Pai +7
In this paper, we contend that a natural objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a low-dimension…
RLIF: Interactive Imitation Learning as Reinforcement Learning
Jianlan Luo, Perry Dong, Yuexiang Zhai +2
Although reinforcement learning methods offer a powerful framework for automatic skill acquisition, for practical learning-based control problems in domains such as robotics, imita…
Investigating the Catastrophic Forgetting in Multimodal Large Language Models
Yuexiang Zhai, Shengbang Tong, Xiao Li +4
Following the success of GPT4, there has been a surge in interest in multimodal large language model (MLLM) research. This line of research focuses on developing general-purpose LL…
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh +5
A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interactio…
Closed-Loop Transcription via Convolutional Sparse Coding
Xili Dai, Ke Chen, Shengbang Tong +9
Autoencoding has achieved great empirical success as a framework for learning generative models for natural images. Autoencoders often use generic deep networks as the encoder or d…
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Qiyang Li, Yuexiang Zhai, Yi Ma +1
Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efficient RL methods generally proposes to address this issue with dedic…
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
Yuexiang Zhai, Christina Baek, Zhengyuan Zhou +2
Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt…
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Sheng Liu, Xiao Li, Yuexiang Zhai +4
Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogona…
Analysis of the Optimization Landscapes for Overcomplete Representation Learning
Qing Qu, Yuexiang Zhai, Xiao Li +2
We study nonconvex optimization landscapes for learning overcomplete representations, including learning (i) sparsely used overcomplete dictionaries and (ii) convolutional dictiona…
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
Yichao Zhou, Haozhi Qi, Yuexiang Zhai +4
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our me…