LAM Simulator: Advancing Large Action Model Training for Agent via Online Exploration and Feedback Simulation

Thai Quoc Hoang, Shirley Kokane, Jianguo Zhang, Tian Lan, Zuxin Liu, Ming Zhu, Jake Grigsby, Michael S Ryoo, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Keywords: LLMs Agent; Self-learning, Reinforcement Learning; Data Generation

TL;DR: Advancing Large Action Model Training for Agent via Online Exploration and Feedback Simulation

Abstract: Large Action Models (LAMs) for AI agents have significant potential, but their development is often constrained by the reliance on supervised learning and manual data curation, which are both time-consuming and costly. To address these limitations, we present the LAM Simulator, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback. This framework includes a curated set of high-quality agentic tasks, a diverse collection of tools, and an interactive environment where agent models can call tools, receive execution responses, and obtain action feedback. Our findings indicate that the LAM Simulator significantly enhances model performance and effectively identifies and addresses potential issues. Specifically, our model, LAM-Sim-8x7B, demonstrates an 18.54\% improvement over its base LAM and significantly outperforms other state-of-the-art alternatives on ToolEval benchmark. Furthermore, we have demonstrated that LLMs lacking in agentic capability can greatly benefit from the implementation of LAM Simulator. Our experiments with a model trained on Mixtral-8x7B-Instruct-v0.1 have yielded a doubling to tripling of performance. Remarkably, the data construction process for training these models requires minimal human intervention, making the LAM Simulator a robust framework for accelerating the development of AI agents.

Primary Area: generative models

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Submission Number: 13418