Research
Human & Machine Intelligence
PHYRE: A New Benchmark for Physical Reasoning
August 15, 2019
Abstract
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles. We test several modern learning algorithms on PHYRE and find that these algorithms fall short in solving the puzzles efficiently. We expect that PHYRE will encourage the development of novel sample-efficient agents that learn efficient but useful models of physics. For code and to play PHYRE for yourself, please visit https://player.phyre.ai.
AUTHORS
Research Topics
Related Publications
May 19, 2026
Human & Machine Intelligence
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Alvin W. M. Tan, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Michael C. Frank, Angel Villar Corrales, Charles-Eric Saint-James, Dongyan Lin, Emmanuel Dupoux, Jiayi Shen, Juan Pino, Mahi Luthra, Martin Gleize, Phillip Rust, Rashel Moritz, Sheila Krogh-Jespersen, Surya Parimi, Tom Fizycki, Vanessa Stark, Yosuke Higuchi, Youssef Benchekroun
May 19, 2026
May 17, 2026
Conversational AI
GIM: Evaluating models via tasks that integrate multiple cognitive domains
Alexandre Rezende, Rohit Patel, Steven McClain
May 17, 2026
May 12, 2026
Human & Machine Intelligence
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
March 17, 2026
NLP
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Niyati Bafna, Ioannis Tsiamas, Mark Duppenthaler, Albert Ventayol-Boada, Alexandre Mourachko, Andrea Caciolai, Arina Turkatenko, Artyom Kozhevnikov, Belen Alastruey, Charles-Eric Saint-James, Chierh CHENG, Christophe Ropers, Cynthia Gao, David Dale, Edan Toledo, Eduardo Sánchez, Gabriel Mejia Gonzalez, Holger Schwenk, Jean Maillard, Joe Chuang, João Maria Janeiro, Kevin Heffernan, Marta R. Costa-jussa, Mary Williamson, Nate Ekberg, Paul-Ambroise Duquenne, Pere Lluís Huguet Cabot, Rashel Moritz, Shireen Yates, Surya Parimi
March 17, 2026
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
March 14, 2019
NLP
On the Pitfalls of Measuring Emergent Communication | Facebook AI Research
Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
March 14, 2019
January 13, 2020
NLP
Scaling up online speech recognition using ConvNets | Facebook AI Research
Vineel Pratap, Qiantong Xu, Jacob Kahn, Gilad Avidov, Tatiana Likhomanenko, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
January 13, 2020
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026