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Paper Link
https://arxiv.org/pdf/2606.24597
Abstract: Qwen-AgentWorld introduces a new class of language world models designed to simulate how environments respond to an agent's actions, enabling agents to reason about future states before interacting with the real world. Rather than focusing solely on decision-making, the framework teaches language models to predict state transitions across seven interactive domains, including software engineering, terminal environments, web browsing, operating systems, Android, search, & MCP tool use, using over 10 million real interaction trajectories. The paper presents a three-stage training pipeline (continual pre-training, supervised fine-tuning, & reinforcement learning) alongside AgentWorldBench, a benchmark for evaluating simulation fidelity. Beyond building a strong world model, the authors demonstrate two complementary applications: using the model as a scalable, controllable environment simulator for reinforcement learning, & using world-model training as a foundation that improves downstream agent performance across a wide range of reasoning & tool-use benchmarks. Together, the work argues that learning to accurately simulate the consequences of actions may become a fundamental capability for building more capable general-purpose AI agents.
Join us at Mox to explore:
If world models become accurate enough to predict the consequences of actions before execution, could they eventually replace large portions of real-world agent training, or will interaction with reality always remain indispensable.
Qwen-AgentWorld argues that agents should learn not only how to act, but also how environments respond. Is world modeling the missing ingredient for general agents, or simply another capability alongside planning & reasoning?
Event Schedule;
7pm to 8pm --> quiet reading time, grab & snack & read! (optional)
8pm to 9pm --> open discussion about reading
9pm --> we have our space for a bit longer, stay to socialize or network!
Our event is hosted within Mox SF, the gracious donors of the space we will meet.
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