417k-param RNN generates all 6,573 frames of Bad Apple from a single initial state
SEBADA321 · reddit · 2026-09-08
An open-source project trains a tiny recurrent dynamical system (417k params, 1.6MB FP32) to autonomously roll out the full 6,573-frame Bad Apple video from a single 64-dim initial state, with no timestamp input at inference. Key training tricks: learned teacher tables for parallel segment training, rollout-horizon curriculum (K=2 to 512), state perturbation noise, second-difference smoothness regularization, and chunked decoding. It runs at >200 FPS on an RTX 4080 with 17.2MB VRAM and successfully extrapolates far beyond its 512-frame training horizon. Code and weights on GitHub.
More from Research
- FactoSR: RL with Factorized 4D Objectives Boosts VLM Spatial Reasoning — HKUST-GZ2 · 2026-09-08
- Stanford paper: general coding agents beat hand-built data agents by up to 37 points — CShorten30 · 2026-09-08
- Dhenu Vision 1.0 hits 94.4% precision, beating every frontier model tested — DevDminGod · 2026-09-08
- NBER paper: automation erodes the meaning of work before it eliminates jobs — ArtificialOther · 2026-09-08
- Training a 441M-Param Text-to-Image Diffusion Model From Scratch on a Single Local GPU — ostrisai · 2026-09-08
- Can current LLM architecture reach AGI? An engineer lays out his doubts — mostly_deterministic · 2026-09-08