[Submitted on 5 Oct 2026]
Title:LRCC: Generalizing Low-Rank Compression with Conditional Computation
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Abstract:Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventional methods use a fixed rank allocation during inference, assigning the same amount of compute regardless of the input token. We introduce Low-Rank Conditional Computation (LRCC), which adds token-dependent computation to pretrained models by training one lightweight router per Transformer block to select among a small set of nested low-rank paths. During training, the low-rank factors remain frozen, and only the routers are optimized. We evaluate LRCC on Llama and Qwen models for language modeling and zero-shot downstream tasks. Within the same average active-parameter budget, LRCC improves the predictive performance over static low-rank compression, including a 7.6 percentage-point gain in average downstream accuracy on Llama-2-7B over static methods. At matched batch-size-1 decoding latency, LRCC improves both perplexity and downstream accuracy on Llama-3.2-1B and remains competitive on Llama-2-7B, without specialized kernels. Finally, we assess the usefulness of assigning a token-wise path by analyzing the routers' path choices.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.08858 [cs.CL]
(or arXiv:2610.08858v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.08858
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Thomas Vaitses Fontanari [view email] [v1] Mon, 5 Oct 2026 09:01:51 UTC (335 KB)
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