arXiv AI
Aug 3
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR
arXiv:2601. 09361v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models.
Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
arXiv:2606. 31813v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) paradigm.
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arXiv AI
Jul 21
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
arXiv:2607. 16252v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models.
arXiv Machine Learning
Sep 14
Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
The paper introduces ISO-LoRA, an optimizer that improves rank utilization in Low‑Rank Adaptation (LoRA) by coupling factor updates through spectral descent on the induced tangent perturbation in weight space. Experiments on GPT‑2 adaptation show that standard optimizers like AdamW concentrate updates in a few singular directions, whereas ISO-LoRA distributes energy more evenly, leading to higher effective rank and better downstream performance across 0.1B‑7B models. The authors provide theoretical guarantees under a stylized spiked‑gradient model and demonstrate that ISO-LoRA consistently outperforms factor‑wise optimizers, especially at moderate‑to‑large LoRA ranks.
Hugging Face Trending Papers
Sep 2
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that treats every LoRA step as a tangent vector of the fixed-rank matrix manifold and takes the spectral-norm steepest-descent step of Muon inside that tangent space, mapping the result back to the factors through a retraction native to the LoRA parametrization.
arXiv Machine Learning
Sep 3
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
LoRA-TSD introduces a new optimizer for low‑rank adaptation (LoRA) that treats each update as a tangent vector on the fixed‑rank matrix manifold and applies a Muon‑style spectral‑norm steepest‑descent step within that tangent space. The method avoids costly full‑matrix operations and offers a retraction that is up to 2.8× cheaper than previous manifold approaches. The authors prove that their surrogate recovers LoRA‑Pro, identify the Riemannian gradient as the natural stationarity measure, and provide the first global convergence guarantees for both LoRA‑Pro and LoRA‑TSD, achieving superior performance across multiple benchmarks with Llama and Qwen models.
arXiv AI
Jun 8
On the Geometry of On-Policy Distillation
arXiv:2606. 07082v1 Announce Type: cross Abstract: On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood.
arXiv AI
3d ago
Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation
arXiv:2605.07111v3 Announce Type: replace-cross Abstract: Recent literature on fine-tuning Large Language Models highlights a fundamental debate. While Full Fine-Tuning (FFT) provides greater represe...
arXiv Machine Learning
Jul 30
Between Gradient and Natural Gradient: A Continuum of LoRA Initializations
arXiv:2607. 26247v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) fine-tunes large pretrained models at a fraction of the cost of full fine-tuning, but its performance depends strongly on how the adapters are initialized.
arXiv Machine Learning
Jul 23
The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability
arXiv:2607. 20301v1 Announce Type: new Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks.
arXiv AI
4d ago
Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition
arXiv:2609.37027v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-...
arXiv Machine Learning
Sep 1
Normalized Low-Rank Adaptation
Normalized Low-Rank Adaptation (NoRA) is a lightweight enhancement to the widely used LoRA technique that normalizes the down‑projection matrices during training. By doing so, NoRA stabilizes early optimization dynamics, accelerates convergence, and improves performance across pretraining, supervised fine‑tuning, and reinforcement learning. The method adds no extra trainable parameters or inference‑time cost, making it broadly applicable.
arXiv Machine Learning
Sep 10
One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning
arXiv:2609.05885v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR...