11-Step Walkthrough: Computing VAEs by Hand Connects Diffusion and RLHF
ProfTomYeh · x · 2026-07-29
Professor Tom Yeh provides an 11-step manual walkthrough of forward inference and gradient computation for Variational Autoencoders (VAEs).
Core Mechanism Breakdown:
- Encoding & Reparameterization: Encodes inputs into latent distributions (mean and variance), introducing random noise while using the reparameterization trick to allow gradients to flow.
- Decoding & Reconstruction: Samples from the latent distribution to reconstruct the input, calculating Mean Squared Error (MSE).
- Gradient Derivation: Shows how the KL divergence gradient simplifies to the mean itself, and the reconstruction gradient simplifies to the difference between prediction and input.
Technical Takeaway: The author highlights that the KL divergence in VAEs is the exact mechanism used in RLHF (like GRPO) to prevent models from drifting, while the reconstruction loss is the foundation of modern diffusion model training. Understanding VAEs bridges the core concepts of both.
More from Research
- Alibaba’s HSCodeComp benchmark finds top AI agents still far below human tariff experts — jiqizhixin · 2026-07-29
- Open weights are turning enterprise AI into specialized intelligence companies can own — bigdata · 2026-07-29
- MIRA: A Fully Hallucinated Multiplayer Rocket League Powered by World Models — mathemagic1an · 2026-07-29
- NeurIPS 2026 workshop targets better evaluation methods for interactive agents — cocoweixu · 2026-07-29
- Video models reveal a “Physics Emergence Zone” where motion direction becomes readable — mathemagic1an · 2026-07-29
- Motion direction forms a ring in a video model’s activation space — mathemagic1an · 2026-07-29