Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Autonomous agents have long been a research focus in academic and industry
communities. Previous research often focuses on training agents with limited knowledge …

Reinforcement learning from human feedback (RLHF) has become a pivot technique in
aligning large language models (LLMs) with human preferences. In RLHF practice …

The recent surge in research interest in applying large language models (LLMs) to decision-
making tasks has flourished by leveraging the extensive world knowledge embedded in …

While large language models (LLMs) have demonstrated impressive performance on a
range of decision-making tasks, they rely on simple acting processes and fall short of broad …

State-of-the-art language models can exhibit impressive reasoning refinement capabilities
on math, science or coding tasks. However, recent work demonstrates that even the best …

Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical performance across a …

Designing reward functions is a longstanding challenge in reinforcement learning (RL); it
requires specialized knowledge or domain data, leading to high costs for development. To …

Large language models (LLMs) have demonstrated notable potential in conducting complex
tasks and are increasingly utilized in various financial applications. However, high-quality …

In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in
improving embodied decision-making processes for agents. While Large Language Models …