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arXiv · 2604.20079

On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks

Aarav Gupta·Gururaj Deshpande·Chandreyi Chakraborty
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Abstract

Auto-regressive Large Language Models (LLMs) achieve strong performance on coding tasks, but incur high memory and inference costs. Diffusion-based language models (d-LLMs) offer bounded inference cost via iterative denoising, but their behavior under post-training quantization (PTQ) has been sparsely explored. We investigate the application and robustness of PTQ techniques, specifically GPTQ and a modified Hessian-Aware Quantization (HAWQ) algorithm, on a diffusion-based coding LLM (CoDA) and observe that these methods applied to CoDA exhibit greater robustness at low bitwidths compared to Qwen3-1.7B, its auto-regressive counterpart, under a standardized evaluation pipeline. We find that in our setup, CoDA exhibits greater robustness at low bitwidths (2-4 bits), with smaller accuracy degradation across HumanEval and MBPP benchmarks. Additionally, mixed-precision configurations derived from HAWQ provide smooth trade-offs across accuracy, latency, and memory. The results suggest that diffusion LLMs may offer advantages for efficient deployment due to more quantization-resilience.

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Aarav Gupta, Gururaj Deshpande, Chandreyi Chakraborty. 2026-04-22. On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks. https://arxiv.org/abs/2604.20079

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