activity
20172026
most citedMastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

1.1k citations · 2.6k across the 19 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL20263 cited

Gemma 4 Technical Report

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…

cs.CL202565 cited

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

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 underst…

cs.CL2024145 cited

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Morgane Riviere, Shreya Pathak +195

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…

cs.CL2024238 cited

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Thomas Mesnard, Cassidy Hardin +105

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…

cs.CL2022243 cited

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…

cs.CL2017150 cited

Grounded Language Learning in a Simulated 3D World

Karl Moritz Hermann, Felix Hill, Simon Green +11

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to com…