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About this paper
Junzhe Shen, Jieru Zhao, Ziwei He, Zhouhan Lin
CoDAR, a continuous diffusion language model, resolves the performance gap between continuous and discrete text generation by addressing the "rounding bottleneck" through a two-stage framework. It generates high-quality continuous embedding sequences and then uses a contextualized autoregressive Transformer decoder to produce discrete tokens, achieving competitive generation fluency and flexible diversity control.
Key insights
- The core limitation of continuous diffusion language models is an "rounding bottleneck," where context-agnostic pointwise decoders inefficiently convert continuous embeddings to discrete tokens.
- A dedicated, context-aware autoregressive Transformer decoder is essential for effectively translating continuous latent representations into coherent discrete text.
- Leveraging advanced numerical solvers allows continuous diffusion models to achieve high-quality generation with significantly fewer sampling steps, improving efficiency.
Summary by alphaXiv.
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