Discrete Stochastic Localization for Non-autoregressive Generation

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

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.

Protocol And Measurement Signals

In this work we show that training alone can substantially improve the step-efficiency of MDLM/ReMDM sampling. HFEPX signals include Automatic Metrics with confidence 0.45. Updated from current HFEPX corpus.

Generated Apr 13, 2026, 9:58 AM · Grounded in abstract + metadata only

Research Summary

Contribution Summary

• In this work we show that training alone can substantially improve the step-efficiency of MDLM/ReMDM sampling.
• We propose DSL (Discrete Stochastic Localization), which trains a single SNR-invariant denoiser across a continuum of corruption levels, bridging intermediate draft noise and mask-style endpoint corruption within one Diffusion Transformer.
• On OpenWebText, DSL fine-tuning yields large MAUVE gains at low step budgets, surpassing the MDLM+ReMDM baseline with \(\sim\)4\times fewer denoiser evaluations, and matches autoregressive quality at high budgets.

Why It Matters For Eval

• On OpenWebText, DSL fine-tuning yields large MAUVE gains at low step budgets, surpassing the MDLM+ReMDM baseline with \(\sim\)4\times fewer denoiser evaluations, and matches autoregressive quality at high budgets.

Researcher Checklist

• Gap: Human feedback protocol is explicit

  No explicit human feedback protocol detected.

• Pass: Evaluation mode is explicit

  Detected: Automatic Metrics

• Pass: Quality control reporting appears

  Detected: Calibration

• Gap: Benchmark or dataset anchors are present

  No benchmark/dataset anchor extracted from abstract.

• Pass: Metric reporting is present

  Detected: latency

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