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Showing 1–4 of 4 results for author: Smoot, S

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  1. arXiv:2507.06261  [pdf, ps, other

    cs.CL cs.AI

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

    Authors: Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, Luke Marris, Sam Petulla, Colin Gaffney, Asaf Aharoni, Nathan Lintz, Tiago Cardal Pais, Henrik Jacobsson, Idan Szpektor, Nan-Jiang Jiang, Krishna Haridasan, Ahmed Omran, Nikunj Saunshi, Dara Bahri, Gaurav Mishra, Eric Chu , et al. (3284 additional authors not shown)

    Abstract: In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal unde… ▽ More

    Submitted 22 July, 2025; v1 submitted 7 July, 2025; originally announced July 2025.

    Comments: 72 pages, 17 figures

  2. arXiv:2503.19786  [pdf, other

    cs.CL cs.AI

    Gemma 3 Technical Report

    Authors: Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penchev, Gaël Liu, Francesco Visin, Kathleen Kenealy, Lucas Beyer, Xiaohai Zhai, Anton Tsitsulin , et al. (191 additional authors not shown)

    Abstract: We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achie… ▽ More

    Submitted 25 March, 2025; originally announced March 2025.

  3. arXiv:2310.13810  [pdf

    cs.LG

    A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

    Authors: Xabi Azagirre, Akshay Balwally, Guillaume Candeli, Nicholas Chamandy, Benjamin Han, Alona King, Hyungjun Lee, Martin Loncaric, Sebastien Martin, Vijay Narasiman, Zhiwei, Qin, Baptiste Richard, Sara Smoot, Sean Taylor, Garrett van Ryzin, Di Wu, Fei Yu, Alex Zamoshchin

    Abstract: To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in rea… ▽ More

    Submitted 13 November, 2023; v1 submitted 20 October, 2023; originally announced October 2023.

  4. arXiv:2010.05154  [pdf, other

    cs.LG cs.AI stat.ML

    Lambda Learner: Fast Incremental Learning on Data Streams

    Authors: Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee, Kirill Talanine, Onkar Dalal, Gungor Polatkan, Sara Smoot, Deepak Kumar

    Abstract: One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generated data streams, machine learning methods perform a balancing act between model complexity, training time, and computational costs. Furthermore, when model freshness is critical, the training of models becomes time-const… ▽ More

    Submitted 28 June, 2021; v1 submitted 11 October, 2020; originally announced October 2020.

    Journal ref: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021