EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

2024-02-17 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

EnvScaler is an automated framework designed to synthesize scalable, diverse, and executable tool-interaction environments for training Large Language Model (LLM) agents. It addresses limitations of real-world access, LLM-simulated environment inconsistencies, and the scalability issues of manually built sandboxes. The framework consists of two main components: SkelBuilder, which constructs environment skeletons through topic mining, logic modeling, and dual-agent quality evaluation; and ScenGenerator, which generates multiple task scenarios, initial state data, and rule-based trajectory validation functions for each environment. EnvScaler was used to synthesize 191 environments and approximately 7,000 scenarios, which were then applied to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Experimental results across three benchmarks, including BFCL-v3 Multi-Turn, Tau-Bench, and ACEBench-Agent, demonstrate that EnvScaler significantly improves LLMs' ability to solve complex tasks requiring multi-turn and multi-tool interactions.

Key takeaway

For NLP engineers and research scientists developing LLM agents, EnvScaler offers a robust solution to the critical challenge of scaling diverse, high-quality training environments. By leveraging its programmatic synthesis capabilities, you can significantly enhance your LLM agents' proficiency in multi-turn, multi-tool interactions, leading to improved performance and generalization across complex real-world tasks. Consider integrating EnvScaler into your training pipeline to overcome limitations of real or manually crafted environments and accelerate agent development.

Key insights

EnvScaler automates the creation of diverse, executable tool-interactive environments for training LLM agents, enhancing multi-turn, multi-tool task-solving capabilities.

Principles

• Automated synthesis overcomes manual environment scaling limitations.
• Dual-agent assessment ensures environment quality and consistency.
• Diverse scenarios improve LLM generalization and adaptability.

Method

EnvScaler uses SkelBuilder for environment skeleton construction via topic mining, logic modeling, and dual-agent assessment, then ScenGenerator creates initial states, tasks, and rule-based validation functions.

In practice

• Use EnvScaler to generate large-scale, diverse training environments.
• Apply SFT and RL with synthetic environments to boost LLM agent performance.
• Incorporate both non-conversational and conversational training patterns.

Topics

• LLM Agents
• Tool-Interactive Environments
• Programmatic Synthesis
• Environment Scaling
• SkelBuilder

Code references

• RUC-NLPIR/EnvScaler

Best for: NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.