arXiv:2602. 12429v2 Announce Type: replace Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces MSign, an optimizer designed to prevent training instability in large language models by restoring the stable rank of weight matrices. It identifies two precursors to gradient explosions—rapid stable rank decline and increased Jacobian alignment—and proves that these jointly cause exponential gradient growth. Experiments on models ranging from 5 M to 3 B parameters show that MSign stops training failures while adding less than 7.0% computational overhead.
arXiv:2606. 28932v1 Announce Type: cross Abstract: Large language models have driven recent progress in language and multimodal AI, yet pre-training them at scale is prohibitively expensive.
arXiv:2608. 13426v1 Announce Type: cross Abstract: Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications.
The paper introduces REP‑LIE, a resource‑efficient pruning method for Transformer models that estimates weight importance using gradients from LoRA low‑rank matrices, avoiding full gradient computation. It incorporates a stability score to iteratively prune unimportant parameters and then fine‑tunes the pruned model with lightweight updates, eliminating the need for full‑parameter optimization. Experiments on medium‑scale encoders and large‑scale generative models such as LLaMA‑7B and Mistral‑7B show that REP‑LIE achieves competitive performance compared to existing pruning approaches.
arXiv:2607. 09204v1 Announce Type: cross Abstract: Pretrained language models often exhibit structured weight spectra, suggesting that training may repeatedly produce similar layerwise and component-wise organization.
arXiv:2607. 03377v1 Announce Type: cross Abstract: The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as model lineage tracing, licensing, and evaluation.