Cover Image

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Arango, Sebastian Pineda, Ferreira, Fabio, Kadra, Arlind, Hutter, Frank, Grabocka, Josif
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interpretable Mesomorphic Networks for Tabular Data
by: Kadra, Arlind, et al.
Published: (2023)

Learning to Order: Task Sequencing as In-Context Optimization
by: Kobiolka, Jan, et al.
Published: (2026)

Tabular Data: Is Deep Learning all you need?
by: Zabërgja, Guri, et al.
Published: (2024)

Ensembling Finetuned Language Models for Text Classification
by: Arango, Sebastian Pineda, et al.
Published: (2024)

End-to-End Compression for Tabular Foundation Models
by: Zabërgja, Guri, et al.
Published: (2026)

POP: Prior-Fitted First-Order Optimization Policies
by: Kobiolka, Jan, et al.
Published: (2026)

Transformers Can Do Bayesian Inference
by: Müller, Samuel, et al.
Published: (2021)

Regularized Neural Ensemblers
by: Arango, Sebastian Pineda, et al.
Published: (2024)

Quickly Tuning Foundation Models for Image Segmentation
by: Das, Breenda, et al.
Published: (2025)

When is Warmstarting Effective for Scaling Language Models?
by: Mallik, Neeratyoy, et al.
Published: (2026)

Hierarchical Transformers are Efficient Meta-Reinforcement Learners
by: Shala, Gresa, et al.
Published: (2024)

Warmstarting for Scaling Language Models
by: Mallik, Neeratyoy, et al.
Published: (2024)

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning
by: Ferreira, Fabio
Published: (2025)

Multi-objective Differentiable Neural Architecture Search
by: Sukthanker, Rhea Sanjay, et al.
Published: (2024)

Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning
by: Abdalla, M. H. I., et al.
Published: (2025)

Transfer Learning for Finetuning Large Language Models
by: Strangmann, Tobias, et al.
Published: (2024)

Quick and Accurate Affordance Learning
by: Scholz, Fedor, et al.
Published: (2024)

Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms
by: Everett, Derek, et al.
Published: (2025)

The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data
by: Baek, Christina, et al.
Published: (2026)

QuickBind: A Light-Weight And Interpretable Molecular Docking Model
by: Treyde, Wojtek, et al.
Published: (2024)

QuickLLaMA: Query-aware Inference Acceleration for Large Language Models
by: Li, Jingyao, et al.
Published: (2024)

Lightweight Correlation-Aware Table Compression
by: Stoian, Mihail, et al.
Published: (2024)

A Quick Introduction to Quantum Machine Learning for Non-Practitioners
by: Evans, Ethan N., et al.
Published: (2024)

QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
by: Dhasade, Akash, et al.
Published: (2023)

Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)

Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
by: Mösching, Alexandre, et al.
Published: (2023)

QF: Quick Feedforward AI Model Training without Gradient Back Propagation
by: Qi, Feng
Published: (2025)

Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts
by: Feng, Jiahai, et al.
Published: (2024)

OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
by: Yue, Sheng, et al.
Published: (2024)

Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
by: Bergner, Benjamin, et al.
Published: (2024)

Finetune-Informed Pretraining Boosts Downstream Performance
by: Faysal, Atik, et al.
Published: (2026)

Tune My Adam, Please!
by: Athanasiadis, Theodoros, et al.
Published: (2025)

One-shot World Models Using a Transformer Trained on a Synthetic Prior
by: Ferreira, Fabio, et al.
Published: (2024)

Pretraining in Actor-Critic Reinforcement Learning for Robot Locomotion
by: Fan, Jiale, et al.
Published: (2025)

Understanding Contextual Recall in Transformers: How Finetuning Enables In-Context Reasoning over Pretraining Knowledge
by: Vasudeva, Bhavya, et al.
Published: (2026)

TMI! Finetuned Models Leak Private Information from their Pretraining Data
by: Abascal, John, et al.
Published: (2023)

Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
by: Ferreira, Fabio, et al.
Published: (2026)

On the Emergence of Cross-Task Linearity in the Pretraining-Finetuning Paradigm
by: Zhou, Zhanpeng, et al.
Published: (2024)

Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
by: Hashemian, Sajjad
Published: (2025)

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
by: Xie, Wanyun, et al.
Published: (2025)