most citedTrial and Error: Exploration-Based Trajectory Optimization for LLM Agents

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cs.CL2024

SimulBench: Evaluating Language Models with Creative Simulation Tasks

Qi Jia, Xiang Yue, Tianyu Zheng +2

We introduce SimulBench, a benchmark designed to evaluate large language models (LLMs) across a diverse collection of creative simulation scenarios, such as acting as a Linux termi…

cs.CL2024★ 2 cited

Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Yifan Song, Da Yin, Xiang Yue +3

Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, r…