BATQuant: Outlier-resilient MXFP4 Quantization via Learnable Block-wise Optimization

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• BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations for MXFP4 quantization.result 70%
  Evidencepartial

  BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations

  Implicationpartial

  The abstract explicitly states 'BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations'.

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• BATQuant recovers up to 96.43% of full-precision performance on multimodal benchmarks.result 70%
  Evidencepartial

  recovering up to 96.43% of full-precision performance on multimodal benchmarks

  Implicationpartial

  The abstract provides a specific performance recovery percentage: 'recovering up to 96.43% of full-precision performance on multimodal benchmarks'.

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• Existing rotation-based PTQ methods suffer from severe performance collapse when applied to MXFP4.technical 70%
  Evidencepartial

  existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4.

  Implicationpartial

  The abstract clearly states the problem with existing methods: 'existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4.'

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• BATQuant restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation.method 70%
  Evidencepartial

  restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation

  Implicationpartial

  The abstract describes a core aspect of BATQuant's methodology: 'BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation'.

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• BATQuant incorporates Block-wise Learnable Clipping to suppress residual outliers.method 70%
  Evidencepartial

  incorporate Block-wise Learnable Clipping to suppress residual outliers.

  Implicationpartial

  The abstract details another component of the BATQuant method: 'incorporate Block-wise Learnable Clipping to suppress residual outliers.'

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• Global and Private Kronecker (GPK) decomposition is used in BATQuant for parameter efficiency.method 70%
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  introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead

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  The abstract mentions the GPK decomposition as a feature for efficiency: 'we introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead'.

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• BATQuant clearly outperforms existing methods across diverse tasks.result 70%
  Evidencepartial

  clearly outperforming existing methods across diverse tasks.

  Implicationpartial

  The abstract provides a comparative statement about performance: 'clearly outperforming existing methods across diverse tasks.'

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• The proposed BATQuant method addresses the fundamental format mismatch issue in MXFP4 quantization.technical 70%
  Evidencepartial

  To address these issues, we propose BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation

  Implicationpartial

  The abstract explains the motivation for BATQuant by referencing the 'fundamental format mismatch' and how BATQuant addresses it.

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