arXiv AI October 23, 2026 By Zhangming Li, Qinghao Hu, Weifan Guan, Yifan Zhang, Peisong Wang, Jian Cheng

NormTune: Norm-Guided Parameter-Efficient Fine-Tuning for Reasoning-Intensive Tasks

NormTune introduces a norm‑guided approach to parameter‑efficient fine‑tuning (PEFT) for reasoning‑intensive tasks, addressing a performance gap caused by under‑adaptation in weight‑update norms. By adaptively strengthening layers with insufficient updates, NormTune consistently improves various PEFT methods across challenging benchmarks. Experiments show significant accuracy gains—up to 24.24% with rank‑8 LoRA on S1K‑1.1—while rank‑256 variants match full fine‑tuning performance.

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 AI.

arXiv AI

Oct 1

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...

By Haozhan Tang, Xiuqi Zhu, Xinyin Zhang, Boxun Li, Virginia Smith, Kevin Kuo

llms fine-tuning data-engineering

arXiv Computation and Language

Aug 27

GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning

GRIP (Granular Reward-guided Interpolation of Parameters) is a lightweight framework that blends a reasoning-oriented large language model with an instruction-tuned model by assigning learnable interpolation ratios to individual modules. The ratios are optimized while keeping both source models frozen, using a reward signal that prefers correct and concise responses. Experiments demonstrate that GRIP improves the accuracy-efficiency trade-off compared to fixed or search-based merging baselines and uncover module-wise fusion patterns linked to efficient reasoning.

By Lam So, Canhui Wu, Han Lin

llms

arXiv AI

Jun 8

The Fine-Tuning Trap: Evaluating Negative Transfer and the Role of PEFT in Sub-1B Mathematical Reasoning

arXiv:2606. 06920v1 Announce Type: cross Abstract: Deploying Small Language Models (SLMs) on edge devices requires efficient fine-tuning strategies that adapt models to new tasks without degrading their general capabilities.

By Rahul Nair, Chun Tao

llms fine-tuning benchmarks

arXiv AI

Aug 11

Thought-Level Beam Search for Reasoning

arXiv:2608. 08020v1 Announce Type: new Abstract: Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shifting the critical question from \emph{how much} compute to spend, to \emph{where} to allocate it.

By Lijie Yang, Hongyin Luo, Tri Dao, Ravi Netravali

efficiency benchmarks

arXiv AI

Aug 7

Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging

arXiv:2608. 05541v1 Announce Type: new Abstract: Evolution Strategy (ES) is a promising alternative to gradient-based fine-tuning for resource-constrained Large Language Model (LLM) reasoning.

By Yu Gu, Zhi Zheng, Yunpeng Ba, Xialiang Tong, Mingxuan Yuan, Zhenkun Wang

llms fine-tuning

arXiv Computation and Language

Sep 23

ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

ChainDoRA is a new parameter‑efficient fine‑tuning framework for large language models that replaces the dense low‑rank factorization of LoRA with a connected Tensor‑Train (TT) chain. By separating weight magnitude and direction and using a TT rank to control representation capacity, ChainDoRA achieves higher average accuracy on seven commonsense reasoning benchmarks while dramatically reducing trainable parameters—down to 5.35 M versus 56 M for LoRA and DoRA. Ablation studies show that the TT parameterization offers controllable trade‑offs between parameter cost and accuracy.

By Ashfak Yeafi, Mehedi Hasan, Md Khairul Islam

llms fine-tuning benchmarks

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Related stories

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation

GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning

The Fine-Tuning Trap: Evaluating Negative Transfer and the Role of PEFT in Sub-1B Mathematical Reasoning

Thought-Level Beam Search for Reasoning

Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging

ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning