Principle:Lucidrains X transformers GPT Variational Autoencoder
| Knowledge Sources | |
|---|---|
| Domains | NLP, Language_Modeling, Variational_Inference |
| Last Updated | 2026-02-08 18:00 GMT |
Overview
Technique that combines a variational autoencoder with a GPT-style decoder, encoding sequences into a continuous Gaussian latent space for controllable autoregressive generation.
Description
The GPT-VAE principle combines the CVAE (Conditional Variational Autoencoder) framework with autoregressive language modeling. An encoder compresses the input sequence into a mean and log-variance, from which a latent vector is sampled via the reparameterization trick. This latent is projected into a conditioning token prepended to the decoder input. The decoder then generates text autoregressively, conditioned on this latent. The training loss combines the standard cross-entropy AR loss with a KL divergence term that regularizes the latent space toward a standard normal prior. A KL floor (free bits) prevents posterior collapse, and latent dropout ensures the decoder can function even without latent information.
Usage
Use this principle when designing language models that need a smooth, continuous latent space for controlling generation characteristics. Applicable to style transfer, diverse generation, interpolation between text modes, and settings where you want to sample different generation behaviors from a learned distribution.
Theoretical Basis
The ELBO objective:
The reparameterization trick:
Pseudo-code Logic:
# Abstract algorithm (NOT real implementation)
# Encode
pooled = encoder(sequence)
mean, log_var = split(linear(pooled))
std = exp(0.5 * log_var)
z = mean + std * randn_like(mean) # reparameterization
# Decode conditioned on z
condition_token = project(z) # shape: (batch, 1, dim)
ar_loss = decoder(sequence, prepend_embeds=condition_token)
# KL loss with floor
kl = 0.5 * (exp(log_var) + mean^2 - log_var - 1)
kl = relu(kl - floor).mean()
total_loss = ar_loss + kl_weight * kl