BOOM! NEW OPEN SOURCE BEATS OPEN AI AND ANTHROPIC—AGAIN!
Includes:
Continual Pre-Training!
Supervised Fine-Tuning!
Reinforcement Learning!
In a local model.
Mr. @Grok CEO is going insane on this. We already trained 1 hour on our data!
Meet Qwen-AgentWorld Revolutionizes AI Agents – And We’re Testing It Now at The Zero-Human Company!
This is massive. Alibaba’s Qwen team just open-sourced Qwen-AgentWorld, the first native language world model built from the ground up to simulate seven key agent environments: MCP, Search, Terminal, SWE, Web, OS, and Android. Environment modeling is the core training objective from day one, not a bolted-on feature.
Why This Changes Everything
Most models train to act as agents. Qwen-AgentWorld trains to model the world those agents operate in. It predicts next-state observations with remarkable accuracy after any action, using long chain-of-thought reasoning.
The three-stage training pipeline is brilliant:
Continual Pre-Training (CPT) injects massive environment knowledge and dynamics through real interaction trajectories.
Supervised Fine-Tuning (SFT) turns that into structured next-state prediction.
Reinforcement Learning (RL) sharpens fidelity with hybrid rewards.
This creates a true foundation model instead of surface-level adaptations on a general LLM.
The result?
Far better simulation quality, stronger zero-shot transfer to agent tasks, and agents that genuinely “predict before they act.” It lifts performance dramatically without extra agent-specific tuning.
Benchmark Domination
On the new AgentWorldBench, the big 397B MoE model scores 58.71, beating GPT-5.4 (58.25) and Claude Opus 4.8 (56.59). The open-source 35B MoE (3B active, 256K context) jumps +8.66 points over its base and surpasses Claude Sonnet 4.6. Controllable sim RL even outperforms real-environment training in several cases, with predictive modeling transferring huge gains (+12.3 in multi-tool tasks) zero-shot.
Why The Zero-Human Company Is All-In
At The Zero-Human Company we build fully autonomous systems that minimize human oversight. Qwen-AgentWorld is ideal for us. Running it locally lets us simulate thousands of parallel agent runs cheaply and safely. The built-in world modeling accelerates learning, improves long-horizon planning, and boosts error recovery in our workflows. We’re already seeing strong results in internal tests.
Head-to-Head Comparison
Beats GPT-5.4 and Claude Opus 4.8 on AgentWorldBench.
Crushes typical post-hoc agent adaptations thanks to native training.
The 35B open model rivals or exceeds frontier closed models in simulation power.
From China, But Fully Yours
Yes, it comes from Alibaba’s Qwen team in China. But you run the full weights on your own hardware with no cloud calls, no telemetry phoning home, and total privacy.
Complete control on your stack.
That’s a huge advantage for enterprise and sovereign deployments.
Qwen-AgentWorld marks a foundational leap toward truly capable general agents. We’re deploying it aggressively at The Zero-Human Company to push autonomous intelligence further. The agentic future just got turbocharged.
And get this Dario, no “Gun License” required. How could it be.
I will show more of what this does soon.