Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

2023-12-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Fudan University and ByteDance Seed researchers propose a novel framework to enhance Large Language Model (LLM) tool-use capabilities through automated environment construction and feedback-driven reinforcement learning (RL). The approach addresses limitations in existing RL frameworks, such as unstable training environments and unreliable reward signals. Their five-stage pipeline generates diverse, high-quality training environments by localizing tool execution, incorporating scenario decomposition, document generation, function integration, and complexity scaling. A verifiable reward mechanism, balancing tool invocation precision and task completion, integrates with preference-based RL algorithms. Experiments on various LLMs, including Qwen2.5 and Qwen3 series, demonstrate over 10% performance gains in tool use across four benchmarks (Ours, ToolHop, $\tau$-bench, RoTBench) without degrading general capabilities. Analysis indicates these improvements stem from updates to lower-layer MLP parameters, enhancing contextual understanding.

Key takeaway

For NLP Engineers and Research Scientists developing LLM agents, this framework offers a robust method to significantly improve tool-use performance. By adopting the automated environment construction and verifiable reward mechanism, you can train models more efficiently and achieve better generalization across diverse tasks, even with open-source LLMs. Consider implementing localized tool execution and a balanced reward function to enhance your models' ability to interact with external environments reliably.

Key insights

Automated environment generation and verifiable rewards significantly boost LLM tool-use performance via reinforcement learning.

Principles

• Local execution ensures stable training environments.
• Reward signals must balance precision and completeness.
• Lower-layer MLP updates drive tool-use improvements.

Method

The method involves a five-stage pipeline for automated environment construction (scenario decomposition, document generation, function integration, complexity scaling, localized deployment) combined with a feedback-driven, preference-based RL training using a verifiable reward function.

In practice

• Generate diverse tool-use scenarios locally for stable training.
• Design reward functions that balance tool precision and task completion.
• Focus on lower-layer MLP parameter updates for tool-use fine-tuning.

Topics

• Large Language Models
• Tool Use
• Reinforcement Learning
• Automated Environment Construction
• Verifiable Reward Mechanisms

Code references

• bytedance/FTRL

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.