LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback

TL;DR

LAM SIMULATOR is a framework that enhances data generation for training Large Action Models by enabling autonomous exploration and feedback-driven learning, leading to substantial performance improvements on benchmark tasks.

Contribution

We introduce LAM SIMULATOR, a novel online exploration framework that generates high-quality training data for LAMs with minimal human intervention.

Findings

Models trained with data from LAM SIMULATOR show up to 49.3% performance improvement.

The framework enables autonomous task exploration and multi-approach discovery.

Significant reduction in human effort for dataset creation.

Abstract

Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback. To address these issues, we present LAM SIMULATOR, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback. Our framework features a dynamic task query generator, an extensive collection of tools, and an interactive environment where Large Language Model (LLM) Agents can call tools and receive real-time feedback. This setup enables LLM Agents to explore and solve tasks autonomously, facilitating the discovery of multiple approaches to tackle any given task. The resulting action trajectory data are then used to create high-quality training datasets for LAMs. Our experiments on popular agentic benchmarks, ToolBench and CRMArena, highlight the effectiveness of LAM SIMULATOR: models trained with self-generated datasets using our framework achieve significant performance gains, up to a 49.3\% improvement over their original baselines. LAM SIMULATOR requires minimal human input during dataset creation, highlighting LAM SIMULATOR's efficiency and effectiveness in speeding up development of AI agents.