Author

Zheng Li

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Jul 2026

QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides

This work identifies activation error, rather than weight error, as the dominant source of FP4 RL instability: weights can be synchronized and aligned by a shared quantization-dequantization path, whereas activations are recomputed online and error is amplified by the coarse E2M1 grid.

Zhengyang Zhuge, Hao Yu, Xin Wang et al. · arXiv.org · 0 citations

#natural language process... Preprint Aug 2026

H-Scale: Hessian-Guided Scale Refinement for NVFP4 Sub-Byte LLM Inference

H-Scale is a lightweight post-processing method for NVFP4 per-group scale refinement that selects hardware-valid group scales using a diagonal second-order proxy derived from calibration activations, thereby targeting layer output perturbation more directly.

Hao Yu, Zheng Li, Dayiheng Liu et al. · 0 citations