ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

Abstract:Weight-only quantization substantially reduces the storage of large language model (LLM) transformer blocks, but practical backends often retain the final language-modeling head (LM-head) in BF16 or FP16. Quantizing this projection naively can strongly perturb the vocabulary-logit distribution. We present ARCHead, a packed LM-head compressor that combines a quantized low-rank core, group-wise INT4 residuals, and a low-rank correction fitted in an activation-derived metric. ARCHead stores no dense BF16 head and reduces persistent LM-head storage by 3.7-3.9x. On Qwen3-8B-Base, it uses 25.6% of BF16 head storage while attaining 1.007 relative perplexity; storage-matched naive INT4 yields 1.14-1.16. Replacing the BF16 head left by AWQ or bitsandbytes adds only 0.006-0.007 cross-entropy, with less than 2% throughput change in our measurements. ARCHead therefore complements block quantizers by compressing the large output projection they can leave untouched. Code is available at this https URL.

13 pages, 4 figures. Submitted to ACL Rolling Review (ARR). Code: this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.02703 [cs.CL]

(or arXiv:2608.02703v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.02703 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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