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RT @Alibaba_Qwen: π£π£ Meet Qwen-AgentWorld β a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE,β¦
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RT @Alibaba_Qwen: π£π£ Meet Qwen-AgentWorld β a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE,β¦
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"text": "π£π£ Meet Qwen-AgentWorld β a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation.\n\nπ€ LLMs are trained to be better agents β better at acting in environments. But nobody has trained them to model the environments themselves.\n\nπΊοΈ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes:\n\n1οΈβ£ Build a foundation model for environment simulation β outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench\n\n2οΈβ£ Investigate how world modeling enhances agent training:\nπ¬ Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments\nπ§ Learning to predict environments (LWM warm-up) makes agents stronger β remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning\n\nπ Paper: https://t.co/Jx2l5RKq71\nπ Blog: https://t.co/7tVcKyhsx2\nπ» GitHub: https://t.co/B5Lvb1UZCn\nπ€ HuggingFace: https://t.co/Kw3QBL1TM5\nπ§© ModelScope: https://t.co/YBnGYgMWWI",
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