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Gumbel Distillation for Parallel Text Generation

Chi Zhang, Xixi Hu, Bo Liu, Qiang Liu · Mar 23, 2026 · Citations: 0

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Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

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 https://github.com/hxixixh/gumbel-distill.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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