arXiv:2510.00294v2 Announce Type: replace 
Abstract: Diffusion Large Language Models (DLLMs) have emerged as a new paradigm of language modeling beyond autoregressive next-token prediction. Thanks to their bidirectional attention mechanism, DLLMs are more capable of capturing the connection of context, and thus show unique advantages in challenges like the famous "reversal curse" or learning under data-constrained scenarios. In addition, taking advantage of their inherent modeling foundations, DLLMs have the great potential of efficient inference with parallel decoding algorithms, which enable multi-token prediction per step. However, the high generation quality often requires the number of decoding steps equal to the sequence length, which performs a one-token-per-step decoding, and existing parallel decoding algorithms, which yield suboptimal decoding paths, bring inference speedup at the cost of non-negligible performance degradation. To overcome this challenge, we introduce Free Draft-and-Verification (FreeDave), a novel fast decoding algorithm tailored for DLLMs that achieves lossless parallel decoding without any model modification or extra modules. Specifically, we propose an algorithm of parallel-decoded candidate generation and verification, which is theoretically guaranteed to use the fewest model forward calls to reproduce the same sequence generated by static decoding when enough computation and memory budget is provided. By extensive evaluations on math reasoning and code generation benchmarks across different DLLMs, FreeDave is proven to boost the inference throughput up to $3.78\times$ without performance degradation.

تقدم ورقة حديثة على arXiv طريقة Free Draft-and-Verification، التي تهدف إلى تحسين نماذج اللغة الكبيرة القائمة على الانتشار (DLLMs). تكتسب هذه النماذج شهرة لقدرتها على فهم السياق بشكل أفضل من النماذج التقليدية، وهو أمر حاسم للمهام التي تكون فيها البيانات محدودة. هذه التطورات مهمة لأنها تعالج التحديات الشائعة في نمذجة اللغة، مما قد يؤدي إلى تطبيقات أكثر فعالية في مجالات متنوعة، من الذكاء الاصطناعي إلى معالجة اللغة الطبيعية.

Un artículo reciente en arXiv presenta Free Draft-and-Verification, un método destinado a mejorar los Modelos de Lenguaje de Difusión (DLLMs). Estos modelos están ganando popularidad por su capacidad para comprender mejor el contexto que los modelos tradicionales, lo cual es crucial para tareas donde los datos son limitados. Este avance es significativo ya que aborda desafíos comunes en la modelización del lenguaje, lo que podría llevar a aplicaciones más efectivas en diversos campos, desde la IA hasta el procesamiento del lenguaje natural.

Un récent article sur arXiv présente Free Draft-and-Verification, une méthode visant à améliorer les Modèles de Langage à Diffusion (DLLMs). Ces modèles gagnent en popularité grâce à leur capacité à mieux comprendre le contexte que les modèles traditionnels, ce qui est crucial pour les tâches où les données sont limitées. Cette avancée est significative car elle répond à des défis courants dans la modélisation du langage, ce qui pourrait conduire à des applications plus efficaces dans divers domaines, de l'IA au traitement du langage naturel.

A recent paper on arXiv introduces Free Draft-and-Verification, a method aimed at enhancing Diffusion Large Language Models (DLLMs). These models are gaining traction for their ability to understand context better than traditional models, which is crucial for tasks where data is limited. This advancement is significant as it addresses common challenges in language modeling, potentially leading to more effective applications in various fields, from AI to natural language processing.

Free Draft-and-Verification: Toward Lossless Parallel Decoding for Diffusion Large Language Models

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