I might have missed it in the readme, but what hardware did the training take place on? This is really neat.

I honestly believe that these neat projects (smallest possible, locally run LLMs) are the real future of AI on the long run.

This is incredibly cool! Fascinating to see looped transformers be necessary for mini-Doom, but not for simpler games.

Is the actor trained on world model's mistakes as well? If so, it should learn to force hallucinations that force it into an easier state.

Very cool. I didn't know NNs were capable of such determinism, that's very interesting. Is this a similar effect to overfitting? Also, it's the same model for all the games, right?

If this runs on a PI... can it run on webgpu ?

Honestly fascinating. I almost feel like you'd be better off avoiding any discussion of the fact that this runs on a Raspberry Pi Pico 2 W because it takes away from the crazy world model capabilities (of course, the fact that this does run on a Pico 2 makes the whole thing much more impressive).

There's gotta be a good way to incorporate this logic into a larger, more "traditional" game in a meaningful way. Seems like an approach for procedural generation that could solve some of the issues usually found with actual procedural generation.

[flagged]

[dead]

[flagged]

[dead]

[dead]