arXiv Machine Learning
JARQ: Joint Alternating Refinement for Quantization
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arXiv AI
Aug 10
ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
arXiv:2608. 07019v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models.
arXiv Machine Learning
Aug 18
SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization
arXiv:2608. 15567v1 Announce Type: new Abstract: Weight-only post-training quantization (PTQ) enables the deployment of large language models under tight memory budgets, but accuracy often collapses at 2-3 bits.
arXiv Machine Learning
Sep 1
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
arXiv:2605.11222v2 Announce Type: replace Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (...
arXiv Machine Learning
Jun 8
AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.
arXiv AI
Jul 28
MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models
arXiv:2607. 23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget.
arXiv AI
Sep 2
REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
REAL-Q introduces a new post‑training quantization approach for large language models that replaces the traditional single closed‑form second‑order solver with a fine‑grained, dynamic block‑wise gradient descent applied after every 128‑column block. By aligning the surrogate loss with the end‑to‑end objective and using a sliding window for smooth cross‑layer transitions, REAL‑Q mitigates error propagation and information misalignment. Experiments on LLaMA‑3.1 and Qwen3 show up to ~49% reduction in end‑to‑end KL divergence compared to state‑of‑the‑art methods.