JARQ Preprint Proposes Alternating Refinement to Improve Group-wise LLM Quantization
arXiv preprint dated 29 September 2026 introduces JARQ, a post-training plug-in refinement for existing group-wise quantizers that alternates scale fitting and code…
- JARQ starts from any group-wise quantizer and alternates joint least-squares scale fits with bounded proposals for integer codes
- Abstract reports lower perplexity in 90 of 96 comparisons and higher accuracy in 23 of 24 configurations across tested hosts and models
- Method preserves original bit width, groups, zero points and inference cost while improving outputs from RTN, GPTQ, OmniQuant and AWQ
Limitations of Standard Group-wise Quantization
Current post-training quantizers for large language models round weights to a fixed grid without refitting the grid to the chosen integer codes.
The preprint abstract identifies three issues: the optimal grid depends on the resulting codes, input correlations couple errors across groups, and useful changes often require coordinated adjustments to many codes at once.
JARQ Refinement Approach
The authors propose JARQ as a plug-in that begins with any group-wise quantizer and solves the underlying bilinear box-constrained mixed-integer least-squares problem.
It alternates a joint least-squares fit of all group scales with bounded proposals that adjust multiple codes within a group together on the current grid.
According to the abstract the solver is backpropagation-free, does not increase the layer-wise objective under exact scale solves, and maintains the host quantizer's bit width, groups, zero points and inference cost.
Reported Empirical Improvements
The abstract states that JARQ lowers perplexity in 90 of 96 comparisons on Llama-2, Llama-3 and Qwen models when applied to RTN, GPTQ, OmniQuant and AWQ hosts.
It reports up to 36 percent reduction in three-bit RTN perplexity, improved mean multiple-choice accuracy in 23 of 24 configurations, and better outputs from QEP, QuaRot and OJBKQ.
The refinement is described as running in under a minute per 7B block.
Preprint Status and Implications
This capture is based solely on the arXiv abstract dated 29 September 2026; the full paper, proofs, implementation details and independent replication are not available.
The evidence supports potential gains in quantized model quality without changing deployment requirements, which can benefit users seeking efficient local inference of open models.
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