7 citations · 18 across the 7 of their papers we have counts for
8 papers
From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation
Quanyu Long, Xiao Chen, Jianda Chen +4
Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. We explor…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
Wen Zheng Terence Ng, Jianda Chen, Sinno Jialin Pan +1
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements. Current Reinforcement Learning-based motion planners r…
Large Language Models Know What Makes Exemplary Contexts
Quanyu Long, Jianda Chen, Wenya Wang +1
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs). By instructing LLMs using few-shot demonstrative examples,…
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
Zichen Chen, Jianda Chen, Ambuj Singh +1
Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge. We address thi…
LMExplainer: Grounding Knowledge and Explaining Language Models
Zichen Chen, Jianda Chen, Yuanyuan Chen +3
Language models (LMs) like GPT-4 are important in AI applications, but their opaque decision-making process reduces user trust, especially in safety-critical areas. We introduce LM…
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
Jianda Chen, Sinno Jialin Pan
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this pro…
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
Haiyan Yin, Jianda Chen, Sinno Jialin Pan
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield…