1 citations · 2 across the 5 of their papers we have counts for
6 papers
Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception
Yulin Wang, Yang Yue, Huanqian Wang +11
Human vision is highly adaptive, efficiently sampling intricate environments by sequentially fixating on task-relevant regions. In contrast, prevailing machine vision models passiv…
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Shenzhi Wang, Le Yu, Chang Gao +15
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanis…
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process
Rui Lu, Yang Yue, Andrew Zhao +2
Multitask Representation Learning (MRL) has emerged as a prevalent technique to improve sample efficiency in Reinforcement Learning (RL). Empirical studies have found that training…
Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing
Huanqian Wang, Yang Yue, Rui Lu +5
Large Language Models (LLMs) have demonstrated great potential as generalist assistants, showcasing powerful task understanding and problem-solving capabilities. To deploy LLMs as…
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training
Yulin Wang, Yang Yue, Rui Lu +3
The superior performance of modern visual backbones usually comes with a costly training procedure. We contribute to this issue by generalizing the idea of curriculum learning beyo…
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Andrew Zhao, Erle Zhu, Rui Lu +3
Humans possess the ability to draw on past experiences explicitly when learning new tasks and applying them accordingly. We believe this capacity for self-referencing is especially…