The paper introduces SoCo, a compression framework that optimizes the singular spectrum of large language models using a learnable diagonal matrix to assign importance scores to SVD components. By progressively refining these scores through a three‑stage training process, SoCo adaptively prunes components while amplifying the importance of remaining ones, achieving a balance between aggressive compression and performance preservation. Experiments on multiple LLMs and benchmarks show that SoCo outperforms state‑of‑the‑art compression methods.
arXiv:2606. 07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression.
arXiv:2606.21847v2 Announce Type: replace-cross Abstract: Low-rank decomposition is a promising compression paradigm for large language models (LLMs), yet its effectiveness hinges on rank budget allo...
arXiv:2607. 18284v1 Announce Type: cross Abstract: To excel at their domain large language models are comprised of billions of parameters.
arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD...
arXiv:2505. 17974v2 Announce Type: replace-cross Abstract: The Fisher information is a fundamental concept for characterizing the sensitivity of parameters in neural networks.
Residual sparsification via output importance (PARSER) is a new compression technique for mixture-of-experts large language models that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error. By introducing output importance, PARSER measures each residual’s contribution to the final expert output and compresses accordingly. Experiments show that PARSER reduces the accuracy gap to the uncompressed model by 1.41× on Qwen and 1.44× on DeepSeek while achieving the same peak memory reduction.
LowRankArena is a standardized evaluation platform for SVD‑based low‑rank compression of large language models, unifying task versions, compression budgets, comparison regimes, and inference measurements. It provides a reproducible pipeline with over 3 TiB of released compressed checkpoints, enabling consistent comparisons across methods. An audit of five representative SVD techniques using LowRankArena shows that prior reported gains are highly conditional, with performance leaders and tiers shifting across backbones and keep ratios, and that nominal low‑rank savings often yield limited end‑to‑end speedups.
arXiv:2607. 03057v1 Announce Type: cross Abstract: The rapid growth in the parameter scale of large language models (LLMs) has created a strong demand for efficient compression techniques.
arXiv:2605.28573v2 Announce Type: replace-cross Abstract: LLM pretraining is extremely costly; therefore, parameter-efficient LLM architectures have recently emerged as a compelling research directio...
arXiv:2510. 00192v3 Announce Type: replace Abstract: Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models, yet its representational capacity often lags behind full fine-tuning.
arXiv:2601. 16991v3 Announce Type: replace-cross Abstract: Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments.
The paper introduces Layer-wise Error Modeling Search (LEMS) and KFAC-SVD to improve low-rank SVD-based compression of large language models. LEMS addresses global rank allocation by using a layer-wise error surrogate and an ILP formulation, while KFAC-SVD enhances decomposition quality by employing token-wise statistics to avoid rank collapse. Experiments on Mistral, Qwen3, and Llama 3 show LEMS yields up to 4.8 pp zero‑shot accuracy gains and KFAC‑SVD achieves an average perplexity improvement of 15%.