[
    {
        "id": "osp-17007",
        "type": "article-journal",
        "title": "SoloQ: Calibration-Free Quantization for Diffusion Language Models",
        "author": [
            {
                "family": "Lee",
                "given": "Donghyun"
            },
            {
                "family": "Ghosh",
                "given": "Arkapravo"
            },
            {
                "family": "Manjunath",
                "given": "Varun"
            },
            {
                "family": "Rhee",
                "given": "Bumjoon Kyle"
            },
            {
                "family": "Kook",
                "given": "Hyunho"
            },
            {
                "family": "Xiao",
                "given": "Shiting"
            },
            {
                "family": "Kim",
                "given": "Youngeun"
            },
            {
                "family": "Panda",
                "given": "Priyadarshini"
            }
        ],
        "URL": "https://omanscience.com/en/articles/soloq-calibration-free-quantization-for-diffusion-language-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based token generation. However, their growing model sizes and high inference costs make efficient deployment challenging: full-sequence denoising repeatedly invokes compute-intensive forward passes, while block-diffusion models additionally introduce a memory-intensive KV-cache. Low-bit weight-activation quantization is therefore attractive, yet existing dLLM post-training quantization methods rely on calibration data despite activation distributions shifting across masking states and denoising steps. We present SoloQ, a calibration-free quantization framework that maps weights and activations into a normalized rotated basis with a predictable marginal distribution, enabling data-independent quantization. SoloQ combines a structured K-RPBH rotation with a lightweight rescaling correction for calibration-free quantization. Its predictable post-rotation distribution supports both distribution-matched codebooks and hardware-native NVFP4. For block-diffusion models, SoloQ further applies commit-time KV-cache quantization to compress persistent states without perturbing the actively denoised block. Across full-sequence dLLMs (LLaDA and Dream) and block-diffusion dLLMs(Fast-dLLM v2 and Nemotron-Labs-Diffusion), SoloQ retains accuracy under 4-bit quantization and outperforms calibration-based baselines on knowledge- and reasoning-intensive benchmarks. With NVFP4, SoloQ reduces peak memory by up to 2.61X and accelerates end-to-end inference by up to 2.24X."
    }
]