u/prime-rick

While testing it with an agentic harness I observed that it created simulations of the prompt and tried to take a couple of steps toward what "could be" the right path forward. It's like an ideal training environment for another LLM—a simulation toward the ideal path.

But on a slower system like mine (30 t/s, Q3 quant), its methods are really time-consuming.

On tasks like "write code under 100 lines of insert topic," the model writes a rough draft, then checks the line count by generating the entire code with line numbers (a simulation of a code editor). An agentic harness would've done that efficiently, called outline\_tool (which is much more efficient in a real system), and counted the lines instead.

I think the original Qwen 3.6 35B A3B is still more efficient for my specific workflow.

Still, this doesn't mean Agent World isn't interesting, because it is interesting. As It's literally GENERATING a simulation.

I can easily imagine someone training an LLM through this peculiar release by Qwen.

This release behaves almost like a self-generated reasoning dataset, which could be useful for training future models.

While testing it with an agentic harness I observed that it created simulations of the prompt and tried to take a couple of steps toward what "could be" the right path forward. It's like an ideal training environment for another LLM—a simulation toward the ideal path.

But on a slower system like mine (30 t/s, Q3 quant), its methods are really time-consuming.

On tasks like "write code under 100 lines of insert topic," the model writes a rough draft, then checks the line count by generating the entire code with line numbers (a simulation of a code editor). An agentic harness would've done that efficiently, called outline_tool (which is much more efficient in a real system), and counted the lines instead.

I think the original Qwen 3.6 35B A3B is still more efficient for my specific workflow.

Still, this doesn't mean Agent World isn't interesting, because it is interesting. As It's literally GENERATING a simulation.

I can easily imagine someone training an LLM through this peculiar release by Qwen.

This release behaves almost like a self-generated reasoning dataset, which could be useful for training future models.