EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL

• URL: http://arxiv.org/abs/2605.18703v1
• Date: Mon, 18 May 2026 17:37:40 GMT
• Title: EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
• Abstract summary: We introduce EnvFactory, a fully automated framework for Agentic Reinforcement Learning (Agentic RL) training.<n>EnvFactory autonomously explores and stateful, executable tool environments from authentic resources.<n>It synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement.<n>It achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including $2$-Bench and VitaBench.
• Score: 54.09410318521061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches depend on costly real-world APIs, hallucination-prone LLM simulators, or synthetic environments that are often single-turn or depend on pre-collected documents. Moreover, synthetic trajectories are frequently over-specified, resembling instruction sequences rather than natural human intents, reducing their effectiveness for RL training. We introduce EnvFactory, a fully automated framework that addresses both challenges. EnvFactory autonomously explores and verifies stateful, executable tool environments from authentic resources, and synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement, producing grounded queries with implicit intents. Using only 85 verified environments across 7 domains, EnvFactory generates 2,575 SFT and RL trajectories. Despite using significantly fewer environments than prior work, which are often 5 times more, EnvFactory achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including $τ^2$-Bench and VitaBench. By fully automating both environment construction and trajectory synthesis, EnvFactory provides a scalable, extensible, and robust foundation for Agentic RL.

Related papers

• EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent [21.572853994120806]
  EnvCraft is an automated framework for synthesizing executable environments and scalable training data.<n>We synthesize 139 interactive environments comprising approximately 20K complex tasks for Agentic RL training.
  arXiv  Detail & Related papers  (2026-09-04T10:13:41Z)
• EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation [16.36266898493489]
  A promising direction is to replace manually crafted environments with LLM-simulated counterparts.<n>This paradigm hinges on an unexamined core assumption: LLMs can accurately simulate environmental feedback.<n>In practice, LLM-simulated environments suffer from hallucinations, logical inconsistencies, and silent state drift failures.
  arXiv  Detail & Related papers  (2026-05-08T05:08:16Z)
• Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning [62.499592503950026]
  Large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments.<n>We propose Agent World Model (AWM), a fully synthetic environment generation pipeline.<n>We scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets.
  arXiv  Detail & Related papers  (2026-02-10T18:55:41Z)
• ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas [13.919124676472022]
  ASTRA is an end-to-end framework for training tool-augmented language model agents.<n>ASTRA integrates scalable data synthesis and verifiable reinforcement learning.<n> Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-01-29T11:22:23Z)
• EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis [101.67583081810136]
  Large language models (LLMs) are expected to be trained to act as agents in various real-world environments.<n>This process relies on rich and varied tool-interaction sandboxes.<n>We propose EnvScaler, an automated framework for scalable tool-interaction environments.
  arXiv  Detail & Related papers  (2026-01-09T14:32:06Z)
• AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning [71.4322853508083]
  Conducting reinforcement learning in simulated environments offers a cost-effective and highly scalable way to enhance language-based agents.<n>Previous work has been limited to semi-automated environment synthesis or tasks lacking sufficient difficulty, offering little breadth or depth.<n>We propose a unified pipeline for automated and scalable synthesis of simulated environments associated with high-difficulty but easily verifiable tasks.
  arXiv  Detail & Related papers  (2025-12-28T09:43:11Z)
• Simulating Environments with Reasoning Models for Agent Training [55.98861707136674]
  Building bespoke environments for training is heavy, brittle, and limits progress.<n>We propose two frameworks: Simia-SFT and Simia-RL.<n>Simia-SFT and Simia-RL enable scalable agent training without environment engineering.
  arXiv  Detail & Related papers  (2025-11-03T18:29:57Z)
• RLFactory: A Plug-and-Play Reinforcement Learning Post-Training Framework for LLM Multi-Turn Tool-Use [50.52940111891476]
  Large language models excel at basic reasoning but struggle with tasks that require interaction with external tools.<n>We present RLFactory, a plug-and-play reinforcement learning framework for multi-round tool use.
  arXiv  Detail & Related papers  (2025-08-31T16:47:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.