LoRA training scripts of the world, unite!

Goodbye cold boot - how we made LoRA Inference 300% faster

PLoRA: Efficient Concurrent LoRA Training for Large Language Models

arXiv:2508. 02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance.

Code Llama: Llama 2 learns to code

Beyond LoRA: Can you beat the most popular fine-tuning technique?

Two-Stage Mixture-of-LoRA for Multi-Task Medical Vision-Language Learning

arXiv:2609.14350v1 Announce Type: new Abstract: Medical vision-language models (VLMs) allow a single model to perform clinical image analysis tasks ranging from diagnosis classification to report gen...

Transformers Stop Thinking Too Early, and a Tiny LoRA Fixes It

The paper shows that pretrained transformers often stop using their depth early, following only a few lines of context. A small rank‑8 LoRA applied to an early layer can extend this chain‑following ability, enabling models like Qwen3‑8B to achieve near‑perfect accuracy on 24‑line chains and significantly longer chains with further training. The LoRA acts as a relay, passing chain identity through middle layers and allowing frozen heads to read further up the chain, with the last useful intervention layer identified in most held‑out models.

Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents

arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.

Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures

arXiv:2607. 21612v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation.

Training CodeParrot 🦜 from Scratch

The Appeal and Reality of Recycling LoRAs with Adaptive Merging

arXiv:2602. 12323v2 Announce Type: replace Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance.

StackLLaMA: A hands-on guide to train LLaMA with RLHF

LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-efficient modality-specific adaptation with sequential fusion, enabling modalities to be integrated in stages without retraining previously learned components.