arXiv:2511.21103v1 Announce Type: new 
Abstract: Diffusion Language Models (DLMs) have recently emerged as a strong alternative to autoregressive language models (LMs). DLMs offer comparable accuracy with faster inference speed via parallel decoding. However, standard DLM decoding strategies relying on high-confidence tokens encounter an inherent information-theoretic bottleneck that restricts decoding progress and ultimately slows generation. We demonstrate both theoretically and empirically that prioritizing high-confidence tokens is inherently inefficient. High-probability tokens carry negligible information and strictly relying on them limits the effective progress made in each decoding round. We prove that the number of decoding rounds must grow linearly with the sample's total information (negative log-likelihood) and inversely with the per-round information budget, establishing a bits-to-rounds principle. We also propose Explore-Then-Exploit (ETE), a training-free decoding strategy that maximizes information throughput and decoding efficiency. ETE combines cross-block decoding with targeted exploration of high-uncertainty tokens to reshape the conditional distribution and trigger cascades of confident predictions. Experiments verify our theoretical bounds and demonstrate that ETE consistently reduces the required number of decoding rounds compared to confidence-only baselines without compromising generation quality.

تسلط التطورات الأخيرة في نماذج اللغة بالانتشار (DLM) الضوء على إدخال استراتيجية جديدة للتشفير تُعرف باسم Explore-Then-Exploit (ETE)، والتي تعالج عدم الكفاءة في طرق التشفير القياسية التي تعتمد على الرموز عالية الثقة. تهدف هذه الطريقة إلى تحسين سرعة وفعالية توليد اللغة من خلال التغلب على الاختناقات النظرية المعلوماتية.

Los recientes avances en los Modelos de Lenguaje por Difusión (DLM) destacan la introducción de una nueva estrategia de decodificación llamada Explore-Then-Exploit (ETE), que aborda las ineficiencias de los métodos de decodificación estándar que dependen de tokens de alta confianza. Este enfoque tiene como objetivo mejorar la velocidad y efectividad de la generación de lenguaje al superar los cuellos de botella inherentes a la teoría de la información.

Des avancées récentes dans les modèles de langage par diffusion (DLM) mettent en lumière l'introduction d'une nouvelle stratégie de décodage appelée Explore-Then-Exploit (ETE), qui s'attaque aux inefficacités des méthodes de décodage standard reposant sur des tokens à haute confiance. Cette approche vise à améliorer la vitesse et l'efficacité de la génération de langage en surmontant les goulets d'étranglement informationnels inhérents.

Recent advancements in Diffusion Language Models (DLMs) highlight the introduction of a new decoding strategy called Explore-Then-Exploit (ETE), which addresses inefficiencies in standard decoding methods that rely on high-confidence tokens. This approach aims to enhance the speed and effectiveness of language generation by overcoming inherent information-theoretic bottlenecks.

From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models

One More Thing in AI – Your Shortcut to AI Mastery

From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models

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