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arXiv · 2512.07173

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

Jucheng Shen·Gaurav Sarkar·Yeonju Ro·Sharath Nittur Sridhar·Zhangyang Wang·Aditya Akella·Souvik Kundu
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Abstract

We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic nature of token unmasking confidence across blocks and steps. Based on this observation, we present a lightweight adaptive approach that controls the generation block size, step size, and threshold based on the average confidence of unmasked tokens. We further reduce softmax overhead by dynamically leveraging a subset of the vocabulary to regulate sampling breadth. CadLLM is a plug-and-play, model-agnostic method compatible with KV-cache-based dLLMs. Extensive experiments on four popular tasks demonstrate that CadLLM yields up to 1.1-2.28x throughput improvement over the state-of-the-art baseline with competitive accuracy.

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Jucheng Shen, Gaurav Sarkar, Yeonju Ro, Sharath Nittur Sridhar, Zhangyang Wang, Aditya Akella, Souvik Kundu. 2026-04-16. Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration. https://arxiv.org/abs/2512.07173

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