Gumbel Distillation for Parallel Text Generation

Abstract:The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non-AR models often sacrifice generation quality as they struggle to model the complex joint distribution of token sequences. To narrow this performance gap, we introduce Gumbel Distillation, a novel distillation technique that enables parallel decoders to learn this distribution effectively. Our method leverages the Gumbel-Max trick to create a deterministic mapping from a latent Gumbel noise space to the output tokens of a high-performing AR teacher. As a model-agnostic technique, Gumbel Distillation seamlessly integrates with diverse parallel decoding architectures, including MDLM and BD3-LM. Experiments on LM1B and OpenWebText show that Gumbel Distillation substantially improves the generation quality of parallel language models, achieving a 30.0% improvement in MAUVE score and 10.5% in generative perplexity over MDLM trained on OpenWebText dataset. Code available at this https URL.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2603.22216 [cs.CL]

(or arXiv:2603.22216v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.22216 Focus to learn more arXiv-issued DOI via DataCite

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