✷ The archive · 11 labs · 1,214 dispatches
The labs
No more opening a dozen official blogs every morning. First-hand releases from OpenAI, Anthropic, DeepMind and the rest, each with its substance pulled out.
OpenAI Plan Online Learn Offline (POLO) Framework
OpenAI introduces the Plan Online Learn Offline (POLO) framework, which combines local model-based control and global value function learning to enable efficient learning in complex simulated control tasks.
OpenAI Random Network Distillation (RND) for Reinforcement Learning
OpenAI introduces Random Network Distillation (RND), a curiosity-driven exploration method that for the first time exceeds average human performance on Montezuma's Revenge without using human demonstrations or emulator state access.
OpenAI Iterated Amplification for Complex Goal Learning
OpenAI proposes iterated amplification, an AI safety technique that enables the specification of complex, beyond-human-scale goals by decomposing tasks into simpler sub-tasks to generate training signals.
OpenAI Scholars 2019 Program Announcement
OpenAI has opened applications for its second cohort of OpenAI Scholars, providing stipends and mentorship to individuals from underrepresented groups to study deep learning and open-source a project.
OpenAI Fellows Winter 2019 and Interns Summer 2019 Programs
OpenAI announced the opening of applications for its Winter 2019 Fellows program and Summer 2019 Internships, designed to integrate researchers from diverse backgrounds and students into its AI research efforts.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
OpenAI introduces FFJORD, a continuous-time invertible generative model that uses Hutchinson's trace estimator to enable unrestricted neural network architectures for scalable density estimation.
OpenAI Scholars 2018 Final Projects
OpenAI published a showcase of final projects from the 2018 Scholars program, featuring diverse applications of machine learning in music generation, intuitive physics, and natural language processing.
OpenAI Five: The International 2018 Results
OpenAI Five competed against world-class professional Dota 2 players at The International 2018, demonstrating high-level gameplay despite losing both matches under 'Real Dota' rules.
OpenAI Large-scale Study of Curiosity-Driven Learning
OpenAI researchers demonstrated that agents trained solely on intrinsic curiosity rewards—based on prediction error—can achieve high performance across 54 benchmark environments without any hand-designed extrinsic rewards.
OpenAI Five Benchmark Results
OpenAI Five won a best-of-three series against a team of 99.95th percentile Dota players, demonstrating advanced AI capabilities in handling complexity and uncertainty.
OpenAI Dactyl: Learning Dexterity through Domain Randomization
OpenAI developed Dactyl, a system that trains a human-like robot hand in simulation to manipulate physical objects with high dexterity, transferring those skills to the real world without fine-tuning.
OpenAI Variational Option Discovery Algorithms
OpenAI introduces Variational Autoencoding Learning of Options by Reinforcement (VALOR) and a curriculum learning approach to stabilize the discovery of diverse behavioral modes in reinforcement learning agents.
OpenAI Scholars 2018: Meet our Scholars
OpenAI introduced its 2018 Scholars cohort, featuring a diverse group of researchers focusing on areas such as reinforcement learning, language modeling, and the intersection of AI and the arts.
OpenAI Five Benchmark
OpenAI announced a benchmark match for OpenAI Five, featuring an expanded hero pool and updated game mechanics to test the AI against high-percentile human players.
Glow: Better Reversible Generative Models
OpenAI introduces Glow, a flow-based generative model using invertible 1x1 convolutions to generate high-resolution images with exact latent-variable inference and efficient sampling.
Learning Montezuma’s Revenge from a Single Demonstration
OpenAI trained an RL agent to achieve a record high score of 74,500 in Montezuma’s Revenge by using a single human demonstration to create a reverse curriculum of starting states.
OpenAI Five
OpenAI Five, a team of five neural networks trained via self-play on Dota 2, began defeating amateur human teams and showed that scaled-up reinforcement learning can handle long-horizon, partially observed tasks without fundamental algorithmic advances.
OpenAI Retro Contest Results
OpenAI concluded its first Retro Contest, where the top-performing AI agents for Sonic the Hedgehog were developed by tuning or extending existing algorithms like PPO and Rainbow DQN.
OpenAI Learning Policy Representations in Multiagent Systems
OpenAI introduces a general learning framework that treats agent modeling as a representation learning problem to understand agent behavior in multiagent systems using minimal interaction data.
OpenAI Improving Language Understanding with Unsupervised Learning
OpenAI demonstrated that a Transformer-based model pre-trained via unsupervised language modeling and then fine-tuned on small supervised datasets can achieve state-of-the-art results across diverse language tasks, including commonsense reasoning.
GamePad: A learning environment for theorem proving
OpenAI introduced GamePad, a system designed to apply machine learning methods to theorem proving within the Coq proof assistant to automate tactic prediction and position evaluation.
OpenAI Fellows Program Fall 2018
OpenAI launched the Fellows program in Fall 2018 to provide a pathway for individuals without formal AI backgrounds to transition into artificial intelligence research.
OpenAI Gym Retro Release
OpenAI has released the full version of Gym Retro, a reinforcement learning platform that expands the available game library to over 1,000 titles across multiple classic consoles to enable research into agent generalization.
OpenAI AI and Compute Analysis
OpenAI analysis reveals that compute used for the largest AI training runs has grown exponentially since 2012, doubling every 3.4 months and far outpacing Moore's Law.
AI Safety via Debate
OpenAI proposes a safety technique that trains AI agents to debate topics to help humans supervise systems that perform tasks beyond human cognitive capabilities.
OpenAI Evolved Policy Gradients (EPG)
OpenAI introduces Evolved Policy Gradients (EPG), a metalearning approach that evolves loss functions to help RL agents generalize to new tasks within a task family.
OpenAI Gotta Learn Fast Benchmark
OpenAI has introduced a new reinforcement learning benchmark based on the Sonic the Hedgehog video game franchise to measure transfer learning and few-shot learning performance.
OpenAI Retro Contest and Gym Retro Release
OpenAI launched the Retro Contest to measure reinforcement learning generalization across unseen video game levels and released Gym Retro, a platform integrating 30 SEGA Genesis and 62 Atari 2600 games into Gym.
OpenAI Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
OpenAI researchers developed a bias-free action-dependent baseline for policy gradient methods to reduce gradient estimate variance, enabling faster learning in high-dimensional action spaces.
OpenAI OT-GAN: Improving GANs using optimal transport
OpenAI introduces Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets that uses a new mini-batch energy distance metric to improve training stability and image generation quality.
OpenAI Hackathon Report March 2018
OpenAI hosted its first hackathon on March 3, 2018, bringing together 100 AI community members to develop projects spanning healthcare, safety, and reinforcement learning.
On First-Order Meta-Learning Algorithms
OpenAI introduces Reptile, a first-order meta-learning algorithm that enables agents to learn quickly on new tasks by optimizing parameter initialization without requiring second-order derivatives.
OpenAI Reptile: A Scalable Meta-Learning Algorithm
OpenAI introduced Reptile, a scalable meta-learning algorithm that enables models to generalize from a small number of examples by maximizing the inner product between gradients of different minibatches from the same task.
OpenAI Scholars Program
OpenAI launched the OpenAI Scholars program to provide stipends and mentorship to individuals from underrepresented groups to study deep learning and open-source a project.
OpenAI Research: Learning to Explore via Meta-Reinforcement Learning
OpenAI researchers introduced E-MAML and E-RL², two meta-reinforcement learning algorithms designed to improve exploration performance in complex environments.
OpenAI Ingredients for Robotics Research
OpenAI released eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay (HER) to enable reinforcement learning from sparse rewards in complex manipulation tasks.
OpenAI Multi-Goal Reinforcement Learning Robotics Environments
OpenAI has released a suite of challenging continuous control robotics tasks integrated with OpenAI Gym to advance research in Multi-Goal Reinforcement Learning.
OpenAI Hackathon March 2018
OpenAI hosted a community hackathon and series of talks on March 3, 2018, in San Francisco to encourage AI learning and project development.
OpenAI Supporters and Organizational Updates
OpenAI announced new donors, advisor appointments, and the departure of Elon Musk from its Board of Directors to avoid potential conflicts with Tesla's AI focus.
OpenAI Preparing for Malicious Uses of AI
OpenAI and partners released a research paper forecasting how AI could be misused by malicious actors and providing recommendations for mitigating these global security threats.
OpenAI Interpretable Machine Learning through Teaching
OpenAI introduces a machine teaching approach that uses a teacher-student neural network framework to identify the most illustrative examples of a concept, ensuring the resulting interpretations are human-interpretable rather than arbitrary.
OpenAI Discovering Types for Entity Disambiguation
OpenAI developed a system that uses a neural network to predict membership in automatically discovered types to improve entity disambiguation accuracy on CoNLL and TAC KBP 2010 datasets.
OpenAI Requests for Research 2.0
OpenAI released Requests for Research 2.0, a collection of seven unsolved technical problems in reinforcement learning and machine learning to encourage community contribution.
Scaling Kubernetes to 2,500 Nodes
OpenAI describes the technical optimizations required to scale a Kubernetes cluster to 2,500 nodes on Azure for deep learning research, addressing bottlenecks in etcd, networking, and image pulls.
OpenAI Block-sparse GPU Kernels
OpenAI has released highly-optimized GPU kernels for block-sparse neural network architectures, enabling models to be wider and deeper while running orders of magnitude faster than cuBLAS or cuSPARSE.
OpenAI Learning Sparse Neural Networks through L0 Regularization
OpenAI introduces a method for L0 norm regularization in neural networks to create sparse models that improve training speed, inference speed, and generalization by using stochastic gates to make weights exactly zero.
OpenAI Interpretable and Pedagogical Examples Research
OpenAI researchers demonstrate that training teacher and student neural networks iteratively, rather than jointly, produces interpretable teaching strategies that can effectively teach both AI and humans.
OpenAI Learning a Hierarchy Research
OpenAI has developed Meta-Learning Shared Hierarchies (MLSH), a reinforcement learning algorithm that automatically discovers high-level actions to solve complex, long-horizon tasks more efficiently than brute-force methods.
OpenAI Generalizing from Simulation
OpenAI developed techniques using dynamics and domain randomization to train robot controllers in simulation that can generalize to physical robots and react to unplanned environmental changes.
OpenAI Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
OpenAI researchers developed a dynamics randomization method that allows robotic control policies trained exclusively in simulation to transfer to real-world hardware without further physical training.