OpenAI releases the full GPT-2 model (774M parameters) following staged releases since February (124M, 355M). Includes open-source legal agreement for model-sharing partnerships and technical report on publication norms coordination with the AI research community.
Microsoft invests $1 billion in OpenAI to support AGI development. The companies partner to build a hardware and software platform on Azure, with Microsoft becoming OpenAI's exclusive cloud provider.
OpenAI publishes research on transfer of adversarial robustness across perturbation types. The study demonstrates that models trained against one adversarial attack type develop partial robustness to other attack types, offering insights for improving AI system defenses.
OpenAI introduces MuseNet, a deep neural network generating 4-minute musical compositions with 10 instruments. The model combines diverse styles (country, Mozart, Beatles) by learning from hundreds of thousands of MIDI files, using the same transformer architecture as GPT-2.
OpenAI Five won two consecutive matches against OG, Dota 2 world champions, at Finals. First AI to defeat world champions in an esports game on livestream, unlike AlphaStar (DeepMind) which failed in public matches.
OpenAI creates OpenAI LP, a new 'capped-profit' company to rapidly increase investments in compute and talent while maintaining checks and balances aligned with its mission.
OpenAI and Google introduce activation atlases, a visualization technique for understanding neural interactions in AI models. This tool helps examine internal decision-making processes to identify weaknesses and investigate failures in deployed systems.
OpenAI releases Neural MMO, a massively multiagent game environment for reinforcement learning. The platform supports large, variable numbers of agents in a persistent, open-ended task, enabling better exploration, divergent niche formation, and greater overall competence.
OpenAI publishes research on computational limitations in robust classification against adversaries. The study demonstrates fundamental trade-offs: improving robustness increases computational costs without performance guarantees. Implications for AI model security.
OpenAI releases CoinRun, a training environment measuring agents' generalization ability in reinforcement learning. The tool balances simplicity and challenge, simpler than Sonic but sufficient to test state-of-the-art algorithms.
OpenAI releases Spinning Up in Deep RL, an educational resource to learn deep reinforcement learning. The project includes code examples, exercises, documentation, and tutorials.
OpenAI introduces FFJORD, a reversible generative model using free-form continuous dynamics to learn complex distributions. The method reduces computational complexity and improves scalability compared to prior approaches.
OpenAI releases large-scale study on curiosity-driven learning. Results show models trained with intrinsic curiosity mechanisms develop more robust and generalizable representations, improving downstream task performance without additional supervision.
OpenAI Five wins best-of-three match against 99.95th percentile Dota players (Blitz, Cap, Fogged, Merlini, MoonMeander) in front of 100,000 concurrent livestream viewers.
OpenAI trained a human-like robot hand to manipulate physical objects with unprecedented dexterity, using reinforcement learning and simulation.
OpenAI introduces Glow, a reversible generative model using invertible 1×1 convolutions. The model generates high-resolution realistic images, supports efficient sampling, and discovers manipulable features. Code and online visualization tool released.
OpenAI Five, a team of five neural networks, has started to defeat amateur human teams at Dota 2.
OpenAI achieves state-of-the-art results across diverse language tasks using a scalable, task-agnostic system combining transformers and unsupervised pre-training. The approach validates the effectiveness of pairing unsupervised pre-training with supervised learning methods.
OpenAI introduces GamePad, a learning environment for theorem proving. The tool enables AI models to learn solving complex mathematical problems through interaction with formal systems.
OpenAI releases full version of Gym Retro, a reinforcement learning research platform. Game catalog expands from ~100 titles (Atari/Sega) to over 1,000 games across multiple emulators. Tool for adding new games included.
OpenAI releases analysis showing that since 2012, compute used in largest AI training runs doubles every 3.4 months (vs 2 years for Moore's Law). This metric has grown over 300,000x in 12 years, confirming compute improvements are critical to AI progress.
OpenAI publishes a variance reduction method for policy gradient algorithms using action-dependent factorized baselines. The technique improves training efficiency by reducing gradient estimator variance, applicable to reinforcement learning models.
OpenAI introduces Reptile, a scalable meta-learning algorithm that samples tasks, applies stochastic gradient descent, and updates initial parameters toward learned parameters. Mathematically similar to first-order MAML, it requires only black-box access to optimizers like SGD or Adam with comparable efficiency and performance.
OpenAI releases a multi-goal reinforcement learning benchmark for robotics. The platform provides complex environments and calls on the research community to develop generalizable solutions for diverse robotic tasks.
OpenAI releases eight simulated robotics environments and a Hindsight Experience Replay baseline implementation developed for internal research. These tools enabled training models that work on physical robots. OpenAI also issues a call for robotics research contributions.
OpenAI releases a paper co-authored with Future of Humanity Institute, CSER, CNAS and EFF forecasting malicious AI misuse scenarios and mitigation strategies. Outcome of one year of sustained collaborative research.
OpenAI describes its Kubernetes infrastructure supporting 2,500 nodes for large-scale AI model training. The article details orchestration, networking, and resource management challenges at this scale.
OpenAI releases highly-optimized GPU kernels for block-sparse neural networks. These kernels outperform cuBLAS and cuSPARSE by orders of magnitude depending on sparsity levels. They achieved state-of-the-art results in sentiment analysis and text/image generation.
OpenAI demonstrates sim-to-real transfer of robotic control using dynamics randomization. A robot trained in simulation with varied physical parameters (friction, mass, latency) successfully performs complex tasks on real hardware without additional tuning.
OpenAI publishes research on nonlinear computation in deep linear networks. The study explores how linear architectures can perform complex calculations despite the absence of traditional nonlinear activation functions.
OpenAI releases two Baselines implementations: A2C (synchronous deterministic variant of A3C) and ACKTR (more sample-efficient RL algorithm than TRPO and A2C, with comparable computational cost per update).
OpenAI demonstrates that self-play catapults ML systems from subhuman to superhuman performance with sufficient compute. Within a month, the system progressed from matching top-ranked players to beating professional pros, with continued improvement. Unlike supervised learning constrained by training data, self-play automatically generates better data as the agent improves.
OpenAI releases RL-Teacher, an open-source implementation for training AIs via occasional human feedback instead of hand-crafted reward functions. The technique aims to develop safe AI systems and applies to reinforcement learning problems where rewards are hard to specify.
OpenAI releases PPO (Proximal Policy Optimization), a class of reinforcement learning algorithms simpler to implement and tune than existing approaches, with comparable or superior performance. PPO has become OpenAI's default RL algorithm.
OpenAI publishes Hindsight Experience Replay (HER), a reinforcement learning method enabling agents to learn from failed experiences by retroactively reframing goals. This technique substantially improves training efficiency on complex tasks.
OpenAI open-sources a high-performance Python library for robotic simulation using the MuJoCo engine, developed from a year of robotics research.
OpenAI and DeepMind develop a preference learning algorithm to infer human objectives without explicit reward functions, reducing risks of undesirable AI behaviors.
OpenAI open-sources Baselines, a suite of reinforcement learning algorithm implementations matching published performance. Initial release includes DQN and three variants, with additional algorithms coming in following months.
OpenAI has developed a robotics system trained entirely in simulation and deployed on a physical robot, capable of learning a new task after observing it once.
OpenAI releases Roboschool, open-source robot simulation software integrated with OpenAI Gym for training motor control agents.