In this report, we introduce the Gemini 1.5 family of models, representing the next generation
of highly compute-efficient multimodal models capable of recalling and reasoning over fine …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

The prevalent deployment of learning from human preferences through reinforcement
learning (RLHF) relies on two important approximations: the first assumes that pairwise …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that
improves over the behavior policy that collected the dataset, while at the same time …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

Open-vocabulary object detection has benefited greatly from pretrained vision-language
models, but is still limited by the amount of available detection training data. While detection …

Denoising diffusion probabilistic models (DDPMs)[Ho et al. 2021] have shown impressive
results on image and waveform generation in continuous state spaces. Here, we introduce …

The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution
before processing them with computer vision models has not yet been successfully …