11 citations · 23 across the 13 of their papers we have counts for
13 papers
RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs
Runlong Zhou, Lefan Zhang, Shang-Chen Wu +29
Reinforcement learning (RL) has emerged as the de-facto paradigm for improving the reasoning capabilities of large language models (LLMs). We have developed RLAX, a scalable RL fra…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Arduin Findeis, Floris Weers, Guoli Yin +3
Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same i…
AXLearn: Modular, Hardware-Agnostic Large Model Training
Mark Lee, Chang Lan, Tom Gunter +34
AXLearn is a production system which facilitates scalable and high-performance training of large deep learning models. Compared to other state-of-art deep learning systems, AXLearn…
Distillation Scaling Laws
Dan Busbridge, Amitis Shidani, Floris Weers +3
We propose a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings mitigate…
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Jason Ramapuram, Federico Danieli, Eeshan Dhekane +8
Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are t…
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +29
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. T…
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
Keen You, Haotian Zhang, Eldon Schoop +5
Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact e…
Controllable Music Production with Diffusion Models and Guidance Gradients
Mark Levy, Bruno Di Giorgi, Floris Weers +2
We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-…
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Erik Daxberger, Floris Weers, Bowen Zhang +7
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of t…
Masked Autoencoding Does Not Help Natural Language Supervision at Scale
Floris Weers, Vaishaal Shankar, Angelos Katharopoulos +2
Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks. Recent wor…
Bias in Automated Image Colorization: Metrics and Error Types
Frank Stapel, Floris Weers, Doina Bucur
We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regio…