6 papers
Offline Reinforcement Learning with Generative Trajectory Policies
Xinsong Feng, Leshu Tang, Chenan Wang +1
Generative models have emerged as a powerful class of policies for offline reinforcement learning (RL) due to their ability to capture complex, multi-modal behaviors. However, exis…
Speculative Sampling with Reinforcement Learning
Chenan Wang, Daniel H. Shi, Haipeng Chen
Inference time latency has remained an open challenge for real world applications of large language models (LLMs). State-of-the-art (SOTA) speculative sampling (SpS) methods for LL…
DIP: Dynamic In-Context Planner For Diffusion Language Models
Yang Li, Han Meng, Chenan Wang +1
Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. However, due to the bidirectional attention mechanism, DLM…
Dynamic Retriever for In-Context Knowledge Editing via Policy Optimization
Mahmud Wasif Nafee, Maiqi Jiang, Haipeng Chen +1
Large language models (LLMs) excel at factual recall yet still propagate stale or incorrect knowledge. In-context knowledge editing offers a gradient-free remedy suitable for black…
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
Shudong Liu, Yiqiao Jin, Cheng Li +6
Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases in p…
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Aiden Lewington, Alekhya Vittalam, Anshumaan Singh +48
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Curre…