Cover Image for 9030 club reading "Qwen Agent World": #60 - ML Paper Reading Group
Cover Image for 9030 club reading "Qwen Agent World": #60 - ML Paper Reading Group
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9030 club reading "Qwen Agent World": #60 - ML Paper Reading Group

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San Francisco, CA
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โ€‹โ˜•๏ธ๐Ÿ“ Paper Link ๐Ÿ“โ˜•๏ธ

โ€‹https://arxiv.org/pdf/2606.24597

โ€‹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, and MCP tool use, using over 10 million real interaction trajectories. The paper presents a three-stage training pipeline (continual pre-training, supervised fine-tuning, and 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, and using world-model training as a foundation that improves downstream agent performance across a wide range of reasoning and 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.

โ€‹***We will have a presenter for this paper, Kion who is a Founder at Experiential Labs, simulating reality for hypothesis testing. Previously Staff Research Scientist at Waabi, where he led the simulation team. PhD in Machine Learning from Georgia Tech.***

โ€‹Join us at Mox to explore:

โ€‹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.

โ€‹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 and reasoning?

โ€‹Event Schedule;

โ€‹7pm to 8pm --> quiet reading time, grab and snack and read! (optional)

โ€‹8pm to 9pm --> open discussion about reading ๐Ÿ“

โ€‹9pm --> we have our space for a bit longer, stay to socialize or network!

โ€‹Our event is hosted within Mox SF, the gracious donors of the space we will meet.

Location
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San Francisco, CA
Avatar for 90/30 Club
Presented by
90/30 Club
We meet weekly in-person to talk about new ML papers! Come and join the discussion!
42 Going