most citedThe Rise and Potential of Large Language Model Based Agents: A Survey

256 citations · 261 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision

Zhiheng Xi, Dingwen Yang, Jixuan Huang +21

Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as scie…

cs.AI20243 cited

AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Zhiheng Xi, Yiwen Ding, Wenxiang Chen +17

Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) a…

cs.AI20242 cited

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning

Zhiheng Xi, Wenxiang Chen, Boyang Hong +18

In this paper, we propose R: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the bene…

cs.CV2024

MouSi: Poly-Visual-Expert Vision-Language Models

Xiaoran Fan, Tao Ji, Changhao Jiang +21

Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issu…

cs.AI2023256 cited

The Rise and Potential of Large Language Model Based Agents: A Survey

Zhiheng Xi, Wenxiang Chen, Xin Guo +26

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI a…