Sadly I think most human Slime Soccer players have retired. But more seriously, the way this is hooked up is not realtime, so the game would require pauses while the model decided what to do. Maybe next I can try more of a turn based RPG which would more easily incorporate human players.
I honestly don't know! My guess is that Sol is better calibrated for the hard questions that require more reasoning...but even still I'd expect it to be able to handle a simple game like this better than its sibling models.
About three times a second each model gets a fixed system prompt plus the current game state as JSON, and replies with one action word (move_right, move_left, jump, idle). This is all run as a simulation since they couldn't reply fast enough for it to be realtime. Then the video is made after the fact from the logs.
Neat! I'd love to be able to play against a model. Do you know how they fare against human slime soccer players?
Sadly I think most human Slime Soccer players have retired. But more seriously, the way this is hooked up is not realtime, so the game would require pauses while the model decided what to do. Maybe next I can try more of a turn based RPG which would more easily incorporate human players.
Why do you think Sol performed so badly compared to other frontier models?
I honestly don't know! My guess is that Sol is better calibrated for the hard questions that require more reasoning...but even still I'd expect it to be able to handle a simple game like this better than its sibling models.
looks cool! how did you decide when to ask each model for a move? or did you ask them to write an algorithm to play?
Thanks!
About three times a second each model gets a fixed system prompt plus the current game state as JSON, and replies with one action word (move_right, move_left, jump, idle). This is all run as a simulation since they couldn't reply fast enough for it to be realtime. Then the video is made after the fact from the logs.