EnvFactory automates LLM tool-use training with synthesized environments

Researchers have developed EnvFactory, an automated framework designed to enhance the tool-use capabilities of large language models through agentic reinforcement learning. This system synthesizes executable tool environments and generates realistic, multi-turn training trajectories from authentic resources. By employing topology-aware sampling and refinement, EnvFactory produces grounded queries with implicit intents, overcoming limitations of previous methods that relied on costly APIs or simplistic synthetic data. The framework has demonstrated significant performance improvements, boosting Qwen3-series models by up to 15% on benchmarks like BFCLv3 and enhancing conversational abilities. AI

影响 Enhances LLM agentic reinforcement learning by providing a scalable method for generating training data and environments, potentially improving performance on complex tasks.

排序理由 Publication of an academic paper detailing a new framework for LLM training. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• BFCLv3
• EnvFactory
• MCP-Atlas
• Qwen3-series
• tau^2-Bench
• VitaBench

• 论文
• 模型发布

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →