[Paper] Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Source: arXiv - 2602.10090v1

Overview

The paper introduces Agent World Model (AWM), a pipeline that automatically generates thousands of fully synthetic, code‑driven environments for training autonomous agents. By providing rich, reliable toolsets and high‑quality observations, AWM lets researchers scale reinforcement‑learning (RL) for multi‑turn, tool‑using agents far beyond what existing benchmark suites can support.

Key Contributions

• Synthetic Environment Generator: A pipeline that creates 1,000 diverse “everyday” environments, each equipped with ~35 programmable tools and backed by structured databases.
• Executable State Transitions: Environments are defined by deterministic code rather than LLM‑based simulation, yielding consistent and reproducible dynamics.
• Efficient Data Collection: Agents can interact with these environments orders of magnitude faster than with real‑world or high‑fidelity simulators.
• Scalable RL Training: Demonstrates large‑scale RL for multi‑turn tool‑use agents trained solely on synthetic worlds.
• Out‑of‑Distribution Generalization: Shows that agents trained on AWM transfer well to three established benchmarks, outperforming models trained directly on those benchmarks.

Methodology

1. Environment Specification: Designers write concise Python‑style scripts that declare objects, actions, and tool APIs. Each script is linked to a relational database that stores the ground‑truth state (e.g., inventory, calendar entries).
2. Tool Suite Generation: For every environment, the pipeline auto‑generates a toolbox (e.g., email client, file system, web search) by wrapping existing open‑source utilities with a uniform API.
3. Observation Rendering: Agents receive structured observations (JSON) plus optional natural‑language summaries, mimicking what a real LLM‑agent would see.
4. Reward Design: Because the underlying state is fully known, reward functions can be defined precisely (e.g., “task completed with ≤ 3 tool calls”).
5. Training Loop: Standard RL algorithms (PPO, DQN, and recent actor‑critic variants) are applied to train agents that learn to plan sequences of tool calls to achieve high‑level goals.

The whole pipeline is open‑source, allowing anyone to add new scenarios or swap out tool implementations.

Results & Findings

• Agents trained exclusively in AWM outperform counterparts trained directly on the target benchmarks by 5–12 percentage points.
• The deterministic nature of the environments reduces variance in learning curves, leading to faster convergence (≈ 30 % fewer training steps).
• Reward functions based on database states prove more stable than heuristic rewards derived from noisy visual feedback.

Practical Implications

• Rapid Prototyping: Developers can spin up a new test world in minutes, experiment with novel tool APIs, and iterate on agent policies without waiting for costly data collection.
• Robust Tool‑Use Agents: By exposing agents to a wide variety of tool combinations, AWM helps produce agents that can generalize to unseen real‑world software stacks (e.g., new SaaS APIs).
• Safer RL Development: Since the environments are fully simulated and sandboxed, there’s no risk of agents performing harmful actions on live services during training.
• Benchmark‑Agnostic Evaluation: Companies can evaluate internal agents on a common synthetic suite before deploying to proprietary systems, reducing integration friction.

Limitations & Future Work

• Domain Coverage: While 1,000 scenarios span many everyday tasks, they still lack high‑fidelity physics or rich visual perception, limiting applicability to robotics or AR/VR domains.
• Tool Realism: The generated tool wrappers are simplified abstractions; subtle bugs or latency characteristics of real APIs are not captured.
• Scalability of Human‑Authored Scripts: Creating diverse, high‑quality environment scripts still requires manual effort; future work could explore LLM‑assisted script generation.
• Transfer to Real Systems: The paper shows promising OOD performance, but a systematic study on fine‑tuning agents in live production environments remains open.

Agent World Model opens a practical pathway for scaling autonomous, tool‑using agents, turning the “environment bottleneck” into a solvable engineering problem.

Authors

• Zhaoyang Wang
• Canwen Xu
• Boyi Liu
• Yite Wang
• Siwei Han
• Zhewei Yao
• Huaxiu Yao
• Yuxiong He

Paper Information

• arXiv ID: 2602.10090v1
• Categories: cs.AI, cs.CL, cs.LG
• Published: February 10, 2026
• PDF: Download PDF