arXiv:2602.10090v3 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) have empowered autonomous agents to perform multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets and obtain high-quality observations. Notably, these environments are
Advances in large language models are enabling more sophisticated autonomous agents, but the bottleneck for scaling their training has become the lack of diverse and reliable environments, which this research directly addresses.
The development of infinite synthetic environments removes a critical constraint on scaling agentic reinforcement learning, accelerating the progress and deployment of AI agents across various domains.
The ability to generate 1,000 diverse, high-quality synthetic environments for agent training overcomes a significant scaling limitation, making robust, general-purpose autonomous agents more feasible.
- · AI Agent developers
- · Cloud infrastructure providers
- · SaaS companies leveraging AI
- · Various industries adopting AI agents
- · Traditional white-collar service providers
The rapid acceleration of AI agent capabilities due to enhanced training environments.
Increased adoption of AI agents across industries, leading to significant automation of complex tasks.
Potential for new economic models based on highly autonomous, self-improving AI systems.
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Read at arXiv cs.CL