SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization

TL;DR - SchurQuant is a backpropagation-free post-training quantization method that improves LLM accuracy under extreme 2-bit weight compression. It outperforms the strongest evaluated baseline by 9.65 percentage points in mean zero-shot accuracy across eight Llama and Qwen models.

• SCHUROPT uses Schur-complement curvature to account for optimal corrections by unquantized suffix weights.
• It alternates scale and zero-point refitting with coordinate descent over discrete integer codes.
• On 2-bit Qwen3-4B, SCHUROPT improves mean zero-shot accuracy by 11.88 percentage points with the GPTQ objective fixed.
• SchurQuant adds teacher reconstruction, reference-weight regularization, residual targets, and token weighting to better align reconstruction with end-model performance.