Text Generation via Continuous Latent Space
LucaAmb · x · 2026-07-17
This repost introduces a new generative modeling paper: Latent Thought Flows.
The author outlines a common trend in current generative models:
- Modalities like images, video, audio, and motion are typically compressed into a continuous latent space, generated there, and then decoded.
- Language remains the only modality heavily reliant on discrete token autoregression.
This work applies the latent approach to text:
- Compressing 256 text tokens into 8 continuous latents.
- Generating latents using a one-step flow model.
- Reconstructing text via an autoregressive decoder.
The author claims this method outperforms a tuned autoregressive baseline in balancing inference compute and generation quality.
Related event: Latent Thought Flows: Text Generation via Continuous Latent Space(2 posts)→
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