6 papers
An Open-Source Training Dataset for Foundation Models for Black-box Optimization
Aaron Klein, Herilalaina Rakotoarison, Luca Thale-Bombien +1
Most black-box optimization methods require extensive hyperparameter tuning, often limiting their ability to generalize across different optimization domains. Foundation models for…
When is Warmstarting Effective for Scaling Language Models?
Neeratyoy Mallik, Maciej Janowski, Johannes Hog +4
Model growth from a given checkpoint aims to accelerate training of a larger model, offering potential resource savings. Despite recent interest, warmstarting has seen limited prac…
Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation
Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik +5
Small Language models (SLMs) offer an efficient and accessible alternative to Large Language Models (LLMs), delivering strong performance while using far fewer resources. We introd…
Evolutionary Architecture Search through Grammar-Based Sequence Alignment
Adri Gómez MartÃn, Felix Möller, Steven McDonagh +5
Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover completely novel and perfo…
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
Lennart Schneider, Martin Wistuba, Aaron Klein +3
Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks, especially in black-box settings where models are only accessible via…
Compressing Large Language Models with Automated Sub-Network Search
Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter +1
Large Language Models (LLMs) demonstrate exceptional reasoning abilities, enabling strong generalization across diverse tasks such as commonsense reasoning and instruction followin…