arXiv:2606. 05484v1 Announce Type: new Abstract: Pipeline parallelism enables training of large language models that exceed single-device memory, yet inter-stage activation communication becomes the dominant bottleneck when trained on low-bandwidth networks.
arXiv:2606. 16384v1 Announce Type: new Abstract: Pretraining language models with extended context windows enhances their ability to leverage rich information during generation.
arXiv:2606. 00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment.
FedLore introduces a communication- and memory-efficient federated learning framework that shares a low-rank optimization basis across clients each round, mitigating subspace fragmentation and enabling exact low-rank aggregation. By refreshing this shared basis across rounds, FedLore allows model updates to exceed the per-round rank budget while maintaining a provable $O(T^{-1/2})$ stationarity bound under standard assumptions. Experiments on vision and language tasks, including federated pre‑training, demonstrate that FedLore outperforms low‑rank adapter baselines and matches or surpasses full‑parameter training while reducing communication and optimizer‑state memory.
arXiv:2506. 01260v3 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.
The paper introduces the "Compression Trinity," a unified framework that jointly applies sparsity, quantization, and low‑rank approximations to compress large language models. It presents several methods—MKOR, SLoPe, OPTIMA, PATCH, and SLiM—that leverage these three pillars to accelerate training, reduce memory bandwidth, and recover accuracy, achieving significant speedups and accuracy gains over existing techniques. The results demonstrate that combining all three compression strategies is essential for efficient, scalable, high‑performance LLM deployment.
arXiv:2609.40127v1 Announce Type: cross Abstract: Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keepin...
MILO is a compression framework that reduces the key-value cache memory used in many-shot in-context learning by applying block-wise low-rank compression. It dynamically allocates rank budgets to blocks based on information entropy, preserving important information while aggressively compressing redundant parts. Experiments on Qwen2.5 models show up to a 50% reduction in KV cache memory and a 1.8× throughput improvement with negligible performance loss on classification and reasoning tasks.
arXiv:2606. 00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deployment challenging on resource-constrained devices.
Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus efficient on standar...
arXiv:2607. 17913v1 Announce Type: cross Abstract: Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead.
The paper proposes a sensitivity‑aware residual‑stream pruning method for large language models that goes beyond minimizing activation reconstruction error. By using a second‑order approximation of output KL divergence, the authors derive a spectral upper bound that selects pruning subspaces based on both activation covariance and output sensitivity, enabling efficient eigendecomposition. Experiments on instruction‑tuned language models show that this approach consistently reduces calibration KL divergence, improves perplexity, and enhances downstream task performance across various compression levels.
arXiv:2505.10202v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their e...