MXFP4 error decomposes into scale bias, deadzone truncation, and grid noise that each dominate distinct RL failure modes, with macro-block scaling, outlier fallback, and adaptive noise recovering or exceeding BF16 performance.
GPTQ : Accurate post-training quantization for generative pre-trained transformers
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Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor
MXFP4 error decomposes into scale bias, deadzone truncation, and grid noise that each dominate distinct RL failure modes, with macro-block scaling, outlier fallback, and adaptive noise recovering or exceeding BF16 performance.