TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.
Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
5 Pith papers cite this work. Polarity classification is still indexing.
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DMax uses On-Policy Uniform Training and Soft Parallel Decoding to enable aggressive parallelism in dLLMs, raising TPF on GSM8K from 2.04 to 5.47 and on MBPP from 2.71 to 5.86 while preserving accuracy.
VRCD prioritizes visually complementary positions during parallel decoding in dMLLMs by measuring attention overlap with the new Visual Redundancy Index, yielding accuracy gains over confidence-based baselines on M^3CoT and MMBench.
Saber improves both speed and accuracy of diffusion language models on code generation by dynamically adjusting unmasking steps and reverting low-confidence tokens via backtracking.
MRP predicts logit residuals between adjacent denoising steps in DLMs from backbone hidden states to support efficient multi-token denoising, yielding up to 1.4x lossless speedup or 22.6-point accuracy gains on code and math tasks.
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DMax: Aggressive Parallel Decoding for dLLMs
DMax uses On-Policy Uniform Training and Soft Parallel Decoding to enable aggressive parallelism in dLLMs, raising TPF on GSM8K from 2.04 to 5.47 and on MBPP from 2.71 to 5.86 while preserving accuracy.
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Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
VRCD prioritizes visually complementary positions during parallel decoding in dMLLMs by measuring attention overlap with the new Visual Redundancy Index, yielding accuracy gains over confidence-based baselines on M^3CoT and MMBench.
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Multi-Token Residual Prediction
MRP predicts logit residuals between adjacent denoising steps in DLMs from backbone hidden states to support efficient multi-token denoising, yielding up to 1.4x lossless speedup or 22.6-point accuracy gains on code and math tasks.