Generating Bad Apple autonomously from a single initial state using a tiny recurrent dynamical system (417k params) [P]

The researchers used a tiny cycle dynamics system with only 417,000 parameters to successfully generate approximately 6,500 frames of full-resolution “Bad Apple” animation from a single initial state. The system employs a 64-dimensional latent space and works in conjunction with a frame decoder through timestamp-free closed-loop recursive transition (CTF). During the inference phase, peak memory usage was around 17.2 MB, and the process ran at over 200 FPS on an RTX 4080 graphics card. During training, techniques such as learned potential teacher tables, rolling horizon learning, state perturbation noise, and second-order differential acceleration regularization were used to address the problems of gradient vanishing and error accumulation. Momentum buffering was also scaled during management after the 500th epoch. Although the model was trained under a long horizon (K=512), it successfully expanded a complete 6.5k sequence, verifying its dynamic stability at a smaller parameter scale.