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Discussion (12 Comments)Read Original on HackerNews
https://www.youtube.com/watch?v=yAEMVVtnUqM
I do wonder if this is an avenue for console gaming that might be practical in a few years; AI-centric hardware that might be too beefy or expensive for regular users, but can extend new or existing games. Kinda like the expansion paks of old.
unfortunate that the "ALE" design wasn't opensourced (couldn't find a link in their post) but I would be interested in learning more about the design, in particular what sort of data pipeline was necessary from skyrim to give this sort of action flexibility?
Ale is what makes this work locally, I felt a little conscious about it as I am not sure if it is a novel approach or somebody comes out and claims I rediscovered BERT or something (though ale runs at 1/10 the cost of BERT).
The moment where the dog is going on about "something foul in the air" as the player is attacked by a wolf ("F--- dude you could have warned me!") was great comedy.
If nothing else - this is how NPCs should work in games moving forward!
An LLM predicts the next token. If you're trying to predict the next token in a mathematics competition, or while playing a deep strategy game, being a much larger and more capable model helps enormously. To predict that next token correctly, the model effectively needs to model a bunch of possible future states - even if that is a second order (unintended) effect, it is what is seems to be happening.
This is basically the Ilya (and Dario) argument that prediction, understanding, and compression are the same thing (deep rabbit hole) from a few years ago.
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In my opinion; this is a beautiful idea, but videogames do not need most of that. Videogames (and games in general) shine when character behavior is predictable, and when NPCs are a little dumb (just a little).
We already have very good small roleplaying models — Qwen 3.5 4B/9B/30B-A3B. Nowhere near frontier models at general reasoning. But they can act and write in a very engaging way. Good at roleplaying but very weak at reasoning. They just need a little nudge at reasoning...
And that's the key. The player has already expressed their intent: attack that guy, go look over there, cover me, find the key that shines and is golden, etc. A constrained world, with a constrained set of actions. Instead of asking the model to reason over an enormous space of possible futures, we're mostly asking it to map: player intent + current world state → a small sequence of plausible actions.
As for the "dump context to an LLM". It's basically. "You are roleplaying as X - you experienced Y - you like/dislike (dispositions) Z, you remember Alpha, your journal says Delta. Player orders you to do Gamma. - "What do you respond and do?"
It kind of works (as you can see in the videos I posted). I am not going against the grain, big models are better, but do we need those models for everything?