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This paper proposes Dynamic-dLLM, a training-free framework that accelerates diffusion large language models by dynamically allocating cache-update budgets and calibrating decoding thresholds, achieving over 3x speedup on models like LLaDA and Dream while maintaining performance.
Speculative decoding is an inference optimization technique that uses a fast draft model to propose future tokens verified in parallel by a larger model, improving LLM generation speed. The article highlights its trending status on Papers with Code and a recent SGLang blog post about state-of-the-art latencies using DFlash models.
PerceptionDLM introduces a multimodal diffusion language model that enables parallel region perception via structured attention masking and efficient prompting, achieving faster inference without sacrificing caption quality. Experiments show competitive performance with substantial speed improvements for multi-region perception tasks.
Analyzes how DiffusionGemma's bidirectional attention and parallel block generation could potentially yield higher valid tool call rates due to its ability to revise tokens, even though its base quality is lower than Gemma 4.
This paper introduces MARS, a stopping rule for parallel LLM test-time scaling that probes partial traces to stop early without sacrificing accuracy, saving 25–47% of tokens across reasoning models on competition math benchmarks.
This paper proposes AXON, a training-free module that improves the quality-latency trade-off of discrete diffusion language model decoding by intelligently selecting 'anchor' tokens to reveal first, using attention, uncertainty, and confidence signals to support subsequent denoising steps. Experiments on reasoning and code-generation benchmarks show AXON reduces function evaluations while maintaining or improving accuracy.
NVIDIA has open-sourced the LocateAnything model, using parallel bounding box decoding technology to predict complete coordinates in one step, fast and accurate. The model has only 3B parameters and can run on consumer-grade GPUs, supporting video object localization, UI recognition, OCR, and other tasks.
Fast-dLLM++ introduces Fréchet profile decoding for diffusion LLMs, a training-free method that selects parallel commit sets based on heterogeneous confidence profiles, achieving up to 37% higher throughput at comparable accuracy on benchmarks with LLaDA-8B.
This paper presents EPIC, an efficient framework for context-free grammar constrained decoding in diffusion language models that reduces inference time by up to 67.5% while maintaining syntactic correctness.
This paper introduces Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) to accelerate inference in diffusion-based large language models by dynamically deciding when tokens have converged and forecasting logit trends, reducing unnecessary denoising steps while preserving output quality.
NVIDIA introduces LocateAnything, a unified generative grounding and detection framework that uses Parallel Box Decoding to improve decoding throughput and localization accuracy. This work will be presented at CVPR 2026.
LocateAnything proposes Parallel Box Decoding for unified visual grounding and object detection, decoding geometric elements as atomic units to improve throughput and localization accuracy, supported by a large-scale dataset of 138M samples.
NVIDIA introduces Nemotron-Labs Diffusion, a family of diffusion language models that generate text in parallel and iteratively refine it, offering faster generation and the ability to revise previous tokens.
NVIDIA released Nemotron-Labs-Diffusion, a family of diffusion language models that generate multiple tokens in parallel, enabling faster inference and better GPU utilization, with sizes from 3B to 14B including vision-language variants.
This paper introduces WINO and WINO+, methods that enable revokable parallel decoding in diffusion LLMs and distill efficient denoising trajectories, significantly improving the quality-speed trade-off.
This paper introduces Parallel Speculative Decoding (PSD), a training-free framework that accelerates diffusion LLM inference by jointly improving spatial and temporal efficiency, achieving up to 5.5× tokens per forward pass with comparable quality to greedy decoding.
dLLM is an open-source library that converts any autoregressive LLM into a diffusion LLM, enabling parallel decoding and faster text generation.
Introduces Orthrus, a method that injects a trainable diffusion attention module into a frozen autoregressive transformer to achieve up to 7.8× tokens per forward pass and ~6× wall-clock speedup on MATH-500, with provably identical output distribution to the base Qwen3-8B model. The approach requires minimal additional parameters and training, and avoids the TTFT penalty of external drafters.
This paper introduces LEAP, a training-free method to accelerate inference in Diffusion Language Models (dLLMs) by detecting early-converging tokens, reducing denoising steps by 30% without losing accuracy.
The Fast Byte Latent Transformer (BLT-D) has been accepted to ICML 2026, introducing a text diffusion method for parallel byte-level decoding to overcome the speed limitations of traditional byte-level language models.