In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
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A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA
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abstract
As large language models (LLMs) have become increasingly compute and memory intensive, parameter-efficient fine-tuning (PEFT) methods are now a common strategy to fine-tune LLMs. A popular PEFT method is Low-Rank Adapters (LoRA), which adds trainable low-rank "adapters" to selected layers. Each adapter consists of a low-rank matrix product, multiplicatively scaled by a rank-dependent factor. This scaling factor, which divides adapters by a factor of the rank, results in slowed learning and stunted performance for LoRA with higher-rank adapters. Consequently, the use of LoRA in practice has generally been limited to very low ranks. In this work, we study the impact of the scaling factor on the learning process and prove that LoRA adapters should be divided by a factor of the square root of the rank. Modifying LoRA with the appropriate scaling factor, which we call the rank-stabilized LoRA (rsLoRA) method, easily provides for a fine-tuning compute/performance trade-off, where larger ranks can be used to trade off increased computational resources during training for better fine-tuning performance, with no change in inference computing cost.
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MatryoshkaLoRA inserts a crafted diagonal matrix P into LoRA to learn accurate nested low-rank adapters that support dynamic rank selection with minimal performance drop.
HAC provides a parameter-efficient way to move CLIP into hyperbolic geometry, yielding consistent gains on zero-shot VQA benchmarks without any VQA training data overlap.
NA-LoRA introduces derivative-based temporal-importance masks and activation-specific step scaling to LoRA to reduce selection misalignment in self-gated FFNs, with reported gains on language and vision-language fine-tuning tasks.
TRIAGE uses dialectical reasoning in LLMs to generate continuous risk scores and explanations for ISMTS, reporting 3.3% AUPRC gain and 81% lower calibration error on three benchmarks.
FedSmoothLoRA improves federated LoRA fine-tuning by constructing local initializations from a round-matching matrix for cross-round continuity and a gradient-aligned matrix for client-specific guidance, yielding faster convergence than prior methods in image and text tasks.
Prefill-only adaptation of LLMs yields 1.9x higher throughput for 512 adapters on Llama 3.1 70B with near-parity performance on RL tasks and recoverable loss on SFT.
Gradient-informed placement of LoRA parameters recovers full performance under GRPO while random placement does not, due to differences in gradient rank and stability across training regimes.
MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.
TLoRA jointly optimizes LoRA initialization via task-data SVD and sensitivity-driven rank allocation, delivering stronger results than standard LoRA across NLU, reasoning, math, code, and chat tasks while using fewer trainable parameters.
A dual-branch foundation-model embedding-difference approach with low-rank adaptation lowers average BSCER100 from 6.16% to 2.17% in cross-database known-attack D-MAD.
Frozen random backbones with low-rank LoRA adapters recover 96-100% of fully trained performance on diverse architectures while training only 0.5-40% of parameters.
With matched data and sizes, CTC+LLM shallow fusion beats tight speech-LLM integration on in-domain ASR, while prefix LLMs win average WER on out-of-domain HuggingFace sets.
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.
ALL-FEM fine-tunes LLMs on a corpus of verified FEniCS scripts and uses multi-agent workflows to automate finite element code generation, achieving 71.79% success on 39 benchmarks across elasticity, flow, and coupled problems.
MLorc compresses optimizer momentum with low-rank methods to enable memory-efficient full fine-tuning of LLMs, outperforming LoRA and GaLore while matching full-parameter performance at small ranks.
Alpha in LoRA outperforms learning-rate scaling, follows a square-root law with rank, and enables a minimalist LoRA-alpha method that improves performance across tasks.
A decoupled four-stage LLM pipeline with rsLoRA, distillation, and CoVe aggregation outperforms larger models on smart contract vulnerability detection and explanation using only 0.6B-4B parameter models.
PEFT adapters are positioned as persistent personal state on foundation models, organized via Scale Up, Scale Down, and Scale Out axes, with MinT as an infrastructure example for managing them.
BaLoRA projects LoRA factors onto a balanced manifold to accelerate convergence by improving conditioning while keeping the same adapted weight matrix.
LoCO is a PEFT technique that constructs orthogonal transformations via low-rank skew-symmetric matrices and compositional rotation chains with a parallelizable approximation, validated on transformer adaptations.
LoRA-Over injects auxiliary parameters into low-rank adapters during training and decomposes them back into standard LoRA at inference, with static or dynamic scheduling to allocate extra capacity where needed, yielding better generalization than vanilla LoRA on GLUE, MT-Bench, GSM8K and HumanEval.
VLA-GSE uses spectral decomposition of the VLA backbone to create generalized and specialized experts, enabling effective robot task adaptation while updating only 2.51% of parameters and achieving 81.2% zero-shot success on LIBERO-Plus.
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
citing papers explorer
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High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
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MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
MatryoshkaLoRA inserts a crafted diagonal matrix P into LoRA to learn accurate nested low-rank adapters that support dynamic rank selection with minimal performance drop.
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HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA
HAC provides a parameter-efficient way to move CLIP into hyperbolic geometry, yielding consistent gains on zero-shot VQA benchmarks without any VQA training data overlap.
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Nonlinearity-Aware LoRA: Structured Gate Adaptation under Low-Rank Constraints
NA-LoRA introduces derivative-based temporal-importance masks and activation-specific step scaling to LoRA to reduce selection misalignment in self-gated FFNs, with reported gains on language and vision-language fine-tuning tasks.
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TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
TRIAGE uses dialectical reasoning in LLMs to generate continuous risk scores and explanations for ISMTS, reporting 3.3% AUPRC gain and 81% lower calibration error on three benchmarks.
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FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation
FedSmoothLoRA improves federated LoRA fine-tuning by constructing local initializations from a round-matching matrix for cross-round continuity and a gradient-aligned matrix for client-specific guidance, yielding faster convergence than prior methods in image and text tasks.
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PreFT: Prefill-only finetuning for efficient inference
Prefill-only adaptation of LLMs yields 1.9x higher throughput for 512 adapters on Llama 3.1 70B with near-parity performance on RL tasks and recoverable loss on SFT.
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Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
Gradient-informed placement of LoRA parameters recovers full performance under GRPO while random placement does not, due to differences in gradient rank and stability across training regimes.
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Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation
MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.
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TLoRA: Task-aware Low Rank Adaptation of Large Language Models
TLoRA jointly optimizes LoRA initialization via task-data SVD and sensitivity-driven rank allocation, delivering stronger results than standard LoRA across NLU, reasoning, math, code, and chat tasks while using fewer trainable parameters.
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DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection
A dual-branch foundation-model embedding-difference approach with low-rank adaptation lowers average BSCER100 from 6.16% to 2.17% in cross-database known-attack D-MAD.
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A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need
Frozen random backbones with low-rank LoRA adapters recover 96-100% of fully trained performance on diverse architectures while training only 0.5-40% of parameters.
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LLMs and Speech: Integration vs. Combination
With matched data and sizes, CTC+LLM shallow fusion beats tight speech-LLM integration on in-domain ASR, while prefix LLMs win average WER on out-of-domain HuggingFace sets.
-
InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flow matching.
-
ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
ALL-FEM fine-tunes LLMs on a corpus of verified FEniCS scripts and uses multi-agent workflows to automate finite element code generation, achieving 71.79% success on 39 benchmarks across elasticity, flow, and coupled problems.
-
MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation
MLorc compresses optimizer momentum with low-rank methods to enable memory-efficient full fine-tuning of LLMs, outperforming LoRA and GaLore while matching full-parameter performance at small ranks.
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The Hidden Power of Scaling Factor in LoRA Optimization
Alpha in LoRA outperforms learning-rate scaling, follows a square-root law with rank, and enables a minimalist LoRA-alpha method that improves performance across tasks.
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Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation
A decoupled four-stage LLM pipeline with rsLoRA, distillation, and CoVe aggregation outperforms larger models on smart contract vulnerability detection and explanation using only 0.6B-4B parameter models.
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On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
PEFT adapters are positioned as persistent personal state on foundation models, organized via Scale Up, Scale Down, and Scale Out axes, with MinT as an infrastructure example for managing them.
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Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
BaLoRA projects LoRA factors onto a balanced manifold to accelerate convergence by improving conditioning while keeping the same adapted weight matrix.
-
LoCO: Low-rank Compositional Rotation Fine-tuning
LoCO is a PEFT technique that constructs orthogonal transformations via low-rank skew-symmetric matrices and compositional rotation chains with a parallelizable approximation, validated on transformer adaptations.
-
Strategic Over-Parameterization for Generalizable Low-Rank Adaptation
LoRA-Over injects auxiliary parameters into low-rank adapters during training and decomposes them back into standard LoRA at inference, with static or dynamic scheduling to allocate extra capacity where needed, yielding better generalization than vanilla LoRA on GLUE, MT-Bench, GSM8K and HumanEval.
-
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
VLA-GSE uses spectral decomposition of the VLA backbone to create generalized and specialized experts, enabling effective robot task adaptation while updating only 2.51% of parameters and achieving 81.2% zero-shot success on LIBERO-Plus.
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SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
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When PCOS Meets Eating Disorders: An Explainable AI Approach to Detecting the Hidden Triple Burden
Small LoRA-tuned open models detect co-occurring PCOS, disordered eating, and body-image distress in social media posts at 75.3% exact-match accuracy with grounded explanations.
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Benchmarking Linguistic Adaptation in Comparable-Sized LLMs: A Study of Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B on Romanized Nepali
Fine-tuning Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B on Romanized Nepali data enables effective generation where zero-shot fails, with Qwen3-8B performing best overall and Llama-3.1-8B showing the largest gains.
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When Top-1 Fails: Calibrating LoRA Monitors for Masked Diffusion LMs
Empirical test shows top-1 argmax concentration has zero precision as collapse warning in DLM LoRA training due to pre-equilibrium saturation while max gradient norm provides usable but family-specific detection on short-horizon runs.
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Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
Mix-MoE applies separate LM and MT expert groups in two post-pretraining stages with Fourier-enhanced routing to reduce parameter interference and improve multilingual MT over baselines.
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Can Muon Fine-tune Adam-Pretrained Models?
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.
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LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language
Qwen2.5-3B was continued-pretrained and then fine-tuned with rsLoRA r256 on Sardinian data to reach 28.5 BLEU into the language, outperforming full fine-tuning and other LoRA variants.
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One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech
Authors submit a cross-lingual voice cloning system to IWSLT 2026 using OmniVoice fine-tuned on ensemble-distilled synthetic data, reporting gains in WER, CER, and speaker similarity for scientific texts in three languages.
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Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization
The paper claims a selective fine-tuning method that identifies and freezes core parameters to mitigate catastrophic forgetting in LLMs while improving domain adaptation, shown in experiments with GPT-J and LLaMA-3.
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Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning
Empirical test of creative quality alignment using ~100 CoT annotations claims architectural duality in LLMs allows appreciation calibration to transfer to generation, explaining data efficiency.
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The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
A survey-style reference book mapping the full agentic-AI stack from transformer internals to production deployment, with no new research result.