Scaling, architecture choices, and post-training curricula matter more than model size alone—a smaller, optimized policy outperforms a larger pretrained one, suggesting careful design beats raw parameter count for complex game-playing tasks.
Faynt introduces transformer-based AI policies for Super Smash Bros. Melee that control all 26 characters with a single model. A 10M-parameter version wins 98.4% of same-character matches against existing AI opponents and beats a zero-delay competitor, using techniques like supervised pretraining on 840k human replays, curriculum learning, and distillation from a larger 75M model.