some questions

[waltonfuture]

Hello. Your work is very interesting and impressive. It has given me a lot of inspiration. I am curious about the boundary of such learning paradigm: can the agents continually benefit from such simulated environments? It seems that we can synthesize infinite training data from agent world model. Where is the upper bound?

Thanks! Hope for your reply and further discussion.

[Raibows]

Hi @waltonfuture ,

Thank you for your interests!

I think the upper bound is not only related to data scale, but model capacity and data diversity, also RL compute.

For AWM, there are three limits can be enhanced:

1. Scenario diversity limit: beyond some point, new environments become structural variations rather than fundamentally new paradigms. Some tail scenarios such as health will not be covered.
2. For tasks, I think we can further expand with harder, compositional, or adversarial tasks derived from the synthesized env interface. Currently, I think there is much room based on our envs.
3. The synthesis pipeline relies on a strong LLM, this acts as a form of capability transfer/distillation (from code to tool-use). A self-evolving setup could push the boundary further (but likely requires much stronger base agents 30B/70B).

Welcome further discussion!

[shahules786]

Hey @Raibows Very cool work, did your team try bench marking any of the SOTA models like (claude-4.6, gpt 5.2, etc) on the synthetic environment?
The ideas introduced in the work are great. I think there are is good scope of future work and improvements here towards creating a much coherent agent world model for LLMs to train/eval on.
Also are you open to any form on future colabs on this work? In that case, please email me at shahul@vibrantlabs.com - would love to explore more!