2 citations · 5 across the 7 of their papers we have counts for
7 papers
FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment
Han Zhao, Jingbo Wang, Wenxuan Song +5
Enabling VLA models to predict environmental dynamics, known as world modeling, has been recognized as essential for improving robotic reasoning and generalization. However, curren…
Advancing General-Purpose Reasoning Models with Modular Gradient Surgery
Min Cai, Yu Liang, Longzheng Wang +6
Reinforcement learning (RL) has played a central role in recent advances in large reasoning models (LRMs), yielding strong gains in verifiable and open-ended reasoning. However, tr…
TRE: Encouraging Exploration in the Trust Region
Chao Huang, Yujing Lu, Quangang Li +8
Entropy regularization is a standard technique in reinforcement learning (RL) to enhance exploration, yet it yields negligible effects or even degrades performance in Large Languag…
The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models
Yan Wang, Yitao Xu, Nanhan Shen +3
Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a pos…
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
Gautham Vasan, Yan Wang, Fahim Shahriar +3
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems,…
RoT: Enhancing Large Language Models with Reflection on Search Trees
Wenyang Hui, Kewei Tu
Large language models (LLMs) have demonstrated impressive capability in reasoning and planning when integrated with tree-search-based prompting methods. However, since these method…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…