[
    {
        "id": "osp-19439",
        "type": "article-journal",
        "title": "LatentQuant: Preserving the Policy-Facing Latent Contract under NVFP4 VAE Quantization",
        "author": [
            {
                "family": "Deng",
                "given": "Ziye"
            },
            {
                "family": "Chen",
                "given": "Lufang"
            },
            {
                "family": "Feng",
                "given": "Shuyu"
            },
            {
                "family": "Duan",
                "given": "Zhenwei"
            },
            {
                "family": "Ye",
                "given": "Zicong"
            },
            {
                "family": "Sun",
                "given": "Yu"
            },
            {
                "family": "Li",
                "given": "Xiaofan"
            },
            {
                "family": "Gan",
                "given": "Ruyi"
            },
            {
                "family": "Wang",
                "given": "Hao"
            },
            {
                "family": "Zhang",
                "given": "Hao"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/latentquant-preserving-the-policy-facing-latent-contract-under-nvfp4-vae-quantization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Recent world action models (WAMs) reuse pretrained video VAEs whose encoder latents directly condition downstream action policies. Quantization must therefore preserve not only reconstruction fidelity but also the policy-facing latent contract expected by the frozen policy. Direct NVFP4 leaves W4A4 quantization error uncompensated, whereas joint quantization-aware training (QAT) can recover reconstruction by moving this representation. On Wan2.1, joint QAT nearly matches FP32 VBench-7 (0.7403 versus 0.7409), yet LIBERO success collapses from 95.5% to 10.5%. Controlled decoder-only experiments show that activation quantize-dequantize operations alter the reconstruction signal and decoder Jacobian, redirecting the gradient returned to the encoder and inducing persistent latent drift. Based on this mechanism, we introduce LatentQuant, a two-stage NVFP4 QAT framework that first aligns the quantized encoder with its high-precision counterpart, then freezes it while adapting the decoder. Across Wan2.1 and Wan2.2, LatentQuant preserves near-baseline control and high reconstruction quality, achieving 95.75% success on LIBERO and 68.8% on RoboTwin. On NVIDIA B300 GPUs, NVFP4 execution achieves 1.17x-1.26x end-to-end VAE speedups over BF16 cuDNN."
    }
]