1 citations · 1 across the 5 of their papers we have counts for
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Online Preference Alignment for Language Models via Count-based Exploration
Chenjia Bai, Yang Zhang, Shuang Qiu +3
Reinforcement Learning from Human Feedback (RLHF) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences. Existing methods perform pr…
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
Chenyou Fan, Chenjia Bai, Zhao Shan +3
Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks. However, existing multi-task planners or policies typically rely on task-specific dem…
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Yang Zhang, Chenjia Bai, Bin Zhao +3
Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a wo…
Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning
Xiaoyu Wen, Chenjia Bai, Kang Xu +4
Cross-domain offline reinforcement learning leverages source domain data with diverse transition dynamics to alleviate the data requirement for the target domain. However, simply m…