{"entity":"openai","name":"OpenAI","kind":"products","summary":"OpenAI's timeline holds 387 products claims from 2016 to 2026. The latest took effect on November 4, 2026.","headline":"The main changes on OpenAI's record: launched Grid Protectors Program on November 2, 2026; released Pando on November 2, 2026; released GPT-6 Rosalind on October 30, 2026; released GPT-6.1 Terra on October 26, 2026; launched Decisions API on October 22, 2026; launched prompt-to-voice on October 16, 2026.","highlights":[{"date":"2026-11-02","kind":"products","text":"launched Grid Protectors Program on November 2, 2026","claims":["clm_143660D3DDBE"]},{"date":"2026-11-02","kind":"products","text":"released Pando on November 2, 2026","claims":["clm_6D0F0CC8F06B"]},{"date":"2026-10-30","kind":"products","text":"released GPT-6 Rosalind on October 30, 2026","claims":["clm_C746984A4B95"]},{"date":"2026-10-26","kind":"products","text":"released GPT-6.1 Terra on October 26, 2026","claims":["clm_0CE4C0EC606A"]},{"date":"2026-10-22","kind":"products","text":"launched Decisions API on October 22, 2026","claims":["clm_D38EF40E1E0C"]},{"date":"2026-10-16","kind":"products","text":"launched prompt-to-voice on October 16, 2026","claims":["clm_CBE432BE9267"]}],"counts":{"total":2200,"by_kind":{"people":31,"money":297,"products":387,"models":65,"other":1420}},"events":[{"date":"2016-04-27","until":null,"published":"2016-04-27","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: OpenAI Gym\", as of 2016-04-27. Claim clm_CC199178F139, https://asof.is/claim/clm_CC199178F139, accessed 2026-11-13. Source: https://openai.com/index/openai-gym-beta.","url":"https://asof.is/claim/clm_CC199178F139","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"OpenAI Gym","status":"valid","assertable":true,"id":"clm_CC199178F139","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms.","url":"https://openai.com/index/openai-gym-beta","read":"feed","excerpt":"We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots to Atari games), and a site for comparing and reproducing results."}},{"date":"2016-12-05","until":null,"published":"2016-12-05","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Universe\", as of 2016-12-05. Claim clm_0353D625C6E1, https://asof.is/claim/clm_0353D625C6E1, accessed 2026-11-13. Source: https://openai.com/index/universe.","url":"https://asof.is/claim/clm_0353D625C6E1","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Universe","status":"valid","assertable":true,"id":"clm_0353D625C6E1","evidence":{"by":"page-read","on":"2026-11-04","quote":"Universe. We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications","url":"https://openai.com/index/universe","read":"feed","excerpt":"We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications."}},{"date":"2017-05-15","until":null,"published":"2017-05-15","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Roboschool\", as of 2017-05-15. Claim clm_CEEA9F81EF9F, https://asof.is/claim/clm_CEEA9F81EF9F, accessed 2026-11-13. Source: https://openai.com/index/roboschool.","url":"https://asof.is/claim/clm_CEEA9F81EF9F","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Roboschool","status":"valid","assertable":true,"id":"clm_CEEA9F81EF9F","evidence":{"by":"page-read","on":"2026-11-04","quote":"Roboschool. We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym","url":"https://openai.com/index/roboschool","read":"feed","excerpt":"We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym."}},{"date":"2017-05-24","until":null,"published":"2017-05-24","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: OpenAI Baselines\", as of 2017-05-24. Claim clm_01F207107777, https://asof.is/claim/clm_01F207107777, accessed 2026-11-13. Source: https://openai.com/index/openai-baselines-dqn.","url":"https://asof.is/claim/clm_01F207107777","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"OpenAI Baselines","status":"valid","assertable":true,"id":"clm_01F207107777","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants","url":"https://openai.com/index/openai-baselines-dqn","read":"feed","excerpt":"We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants."}},{"date":"2017-07-20","until":null,"published":"2017-07-20","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Proximal Policy Optimization\", as of 2017-07-20. Claim clm_EC9EEBD03570, https://asof.is/claim/clm_EC9EEBD03570, accessed 2026-11-13. 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PPO has become the default reinforcement learning algorithm at OpenAI because of i"}},{"date":"2017-08-03","until":null,"published":"2017-08-03","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: RL-Teacher\", as of 2017-08-03. Claim clm_6DC1EF4EB712, https://asof.is/claim/clm_6DC1EF4EB712, accessed 2026-11-13. Source: https://openai.com/index/gathering-human-feedback.","url":"https://asof.is/claim/clm_6DC1EF4EB712","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"RL-Teacher","status":"valid","assertable":true,"id":"clm_6DC1EF4EB712","evidence":{"by":"page-read","on":"2026-11-04","quote":"RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions.","url":"https://openai.com/index/gathering-human-feedback","read":"feed","excerpt":"RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard"}},{"date":"2017-08-18","until":null,"published":"2017-08-18","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: A2C\", as of 2017-08-18. 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ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, and requir"}},{"date":"2017-08-18","until":null,"published":"2017-08-18","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: ACKTR\", as of 2017-08-18. Claim clm_FBF9AD08DA29, https://asof.is/claim/clm_FBF9AD08DA29, accessed 2026-11-13. Source: https://openai.com/index/openai-baselines-acktr-a2c.","url":"https://asof.is/claim/clm_FBF9AD08DA29","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"ACKTR","status":"valid","assertable":true,"id":"clm_FBF9AD08DA29","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C.","url":"https://openai.com/index/openai-baselines-acktr-a2c","read":"feed","excerpt":"We’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) which we’ve found gives equal performance. ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, and requir"}},{"date":"2017-12-06","until":null,"published":"2017-12-06","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Block-sparse GPU kernels\", as of 2017-12-06. Claim clm_5B2E908D8595, https://asof.is/claim/clm_5B2E908D8595, accessed 2026-11-13. Source: https://openai.com/index/block-sparse-gpu-kernels.","url":"https://asof.is/claim/clm_5B2E908D8595","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Block-sparse GPU kernels","status":"valid","assertable":true,"id":"clm_5B2E908D8595","evidence":{"by":"page-read","on":"2026-11-04","quote":"Block-sparse GPU kernels. We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights.","url":"https://openai.com/index/block-sparse-gpu-kernels","read":"feed","excerpt":"We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art results"}},{"date":"2018-02-26","until":null,"published":"2018-02-26","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Multi-Goal Reinforcement Learning\", as of 2018-02-26. Claim clm_8C357F348F76, https://asof.is/claim/clm_8C357F348F76, accessed 2026-11-13. Source: https://openai.com/index/multi-goal-reinforcement-learning.","url":"https://asof.is/claim/clm_8C357F348F76","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Multi-Goal Reinforcement Learning","status":"valid","assertable":true,"id":"clm_8C357F348F76","evidence":{"by":"page-read","on":"2026-11-04","quote":"Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research","url":"https://openai.com/index/multi-goal-reinforcement-learning","read":"feed","excerpt":"Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research"}},{"date":"2018-02-26","until":null,"published":"2018-02-26","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Baselines implementation of Hindsight Experience Replay\", as of 2018-02-26. Claim clm_DB4398BF68F2, https://asof.is/claim/clm_DB4398BF68F2, accessed 2026-11-13. Source: https://openai.com/index/ingredients-for-robotics-research.","url":"https://asof.is/claim/clm_DB4398BF68F2","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Baselines implementation of Hindsight Experience Replay","status":"valid","assertable":true,"id":"clm_DB4398BF68F2","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year.","url":"https://openai.com/index/ingredients-for-robotics-research","read":"feed","excerpt":"We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots. We’re also releasing a set of requests for robotics res"}},{"date":"2018-05-25","until":null,"published":"2018-05-25","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Gym Retro\", as of 2018-05-25. Claim clm_9C2B7F7F2BC9, https://asof.is/claim/clm_9C2B7F7F2BC9, accessed 2026-11-13. Source: https://openai.com/index/gym-retro.","url":"https://asof.is/claim/clm_9C2B7F7F2BC9","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Gym Retro","status":"valid","assertable":true,"id":"clm_9C2B7F7F2BC9","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games.","url":"https://openai.com/index/gym-retro","read":"feed","excerpt":"We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games to over 1,000 games across a variety of backing emulators. We’re also releasing the tool we use to add new"}},{"date":"2018-05-30","until":null,"published":"2018-05-30","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI launched: OpenAI Fellows\", as of 2018-05-30. Claim clm_26B32E962AB7, https://asof.is/claim/clm_26B32E962AB7, accessed 2026-11-13. Source: https://openai.com/index/openai-fellows.","url":"https://asof.is/claim/clm_26B32E962AB7","accessed":"2026-11-13"},"kind":"products","predicate":"launched","value":"OpenAI Fellows","status":"valid","assertable":true,"id":"clm_26B32E962AB7","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI","url":"https://openai.com/index/openai-fellows","read":"feed","excerpt":"We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI."}},{"date":"2018-06-25","until":null,"published":"2018-06-25","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: OpenAI Five\", as of 2018-06-25. Claim clm_55E8CEDA934D, https://asof.is/claim/clm_55E8CEDA934D, accessed 2026-11-13. Source: https://openai.com/index/openai-five-finals.","url":"https://asof.is/claim/clm_55E8CEDA934D","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"OpenAI Five","status":"valid","assertable":true,"id":"clm_55E8CEDA934D","evidence":{"by":"page-read","on":"2026-11-04","quote":"OpenAI Five. Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2","url":"https://openai.com/index/openai-five","read":"feed","excerpt":"Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2."}},{"date":"2018-07-09","until":null,"published":"2018-07-09","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Glow\", as of 2018-07-09. Claim clm_EEC2833EF28D, https://asof.is/claim/clm_EEC2833EF28D, accessed 2026-11-13. Source: https://openai.com/index/glow.","url":"https://asof.is/claim/clm_EEC2833EF28D","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Glow","status":"valid","assertable":true,"id":"clm_EEC2833EF28D","evidence":{"by":"page-read","on":"2026-11-04","quote":"We introduce Glow, a reversible generative model which uses invertible 1x1 convolutions.","url":"https://openai.com/index/glow","read":"feed","excerpt":"We introduce Glow, a reversible generative model which uses invertible 1x1 convolutions. It extends previous work on reversible generative models and simplifies the architecture. Our model can generate realistic high resolution images, supports efficient sampling, and discovers features that can be"}},{"date":"2018-07-30","until":null,"published":"2018-07-30","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Learning dexterity\", as of 2018-07-30. Claim clm_625878BBBB02, https://asof.is/claim/clm_625878BBBB02, accessed 2026-11-13. Source: https://openai.com/index/learning-dexterity.","url":"https://asof.is/claim/clm_625878BBBB02","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Learning dexterity","status":"valid","assertable":true,"id":"clm_625878BBBB02","evidence":{"by":"page-read","on":"2026-11-04","quote":"Learning dexterity. We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity","url":"https://openai.com/index/learning-dexterity","read":"feed","excerpt":"We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity."}},{"date":"2018-11-07","until":null,"published":"2018-11-07","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: energy-based model\", as of 2018-11-07. Claim clm_577CCB1046A4, https://asof.is/claim/clm_577CCB1046A4, accessed 2026-11-13. Source: https://openai.com/index/learning-concepts-with-energy-functions.","url":"https://asof.is/claim/clm_577CCB1046A4","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"energy-based model","status":"valid","assertable":true,"id":"clm_577CCB1046A4","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points.","url":"https://openai.com/index/learning-concepts-with-energy-functions","read":"feed","excerpt":"We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use"}},{"date":"2018-11-08","until":null,"published":"2018-11-08","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Spinning Up in Deep RL\", as of 2018-11-08. Claim clm_4F9D5FCD62E6, https://asof.is/claim/clm_4F9D5FCD62E6, accessed 2026-11-13. Source: https://openai.com/index/spinning-up-in-deep-rl.","url":"https://asof.is/claim/clm_4F9D5FCD62E6","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Spinning Up in Deep RL","status":"valid","assertable":true,"id":"clm_4F9D5FCD62E6","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning.","url":"https://openai.com/index/spinning-up-in-deep-rl","read":"feed","excerpt":"We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials."}},{"date":"2018-12-06","until":null,"published":"2018-12-06","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: CoinRun\", as of 2018-12-06. 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CoinRun strikes a desirable balance in complexity: the environment is simpler"}},{"date":"2019-02-14","until":null,"published":"2019-02-14","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Better language models\", as of 2019-02-14. Claim clm_9360D2779C03, https://asof.is/claim/clm_9360D2779C03, accessed 2026-11-13. Source: https://openai.com/index/better-language-models.","url":"https://asof.is/claim/clm_9360D2779C03","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Better language models","status":"valid","assertable":true,"id":"clm_9360D2779C03","evidence":{"by":"page-read","on":"2026-11-04","quote":"Better language models and their implications. We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text","url":"https://openai.com/index/better-language-models","read":"feed","excerpt":"We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task"}},{"date":"2019-03-04","until":null,"published":"2019-03-04","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Neural MMO\", as of 2019-03-04. Claim clm_4EF687E6885F, https://asof.is/claim/clm_4EF687E6885F, accessed 2026-11-13. Source: https://openai.com/index/neural-mmo.","url":"https://asof.is/claim/clm_4EF687E6885F","accessed":"2026-11-13"},"kind":"products","predicate":"released","value":"Neural MMO","status":"valid","assertable":true,"id":"clm_4EF687E6885F","evidence":{"by":"page-read","on":"2026-11-04","quote":"We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents.","url":"https://openai.com/index/neural-mmo","read":"feed","excerpt":"We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation,"}},{"date":"2019-04-25","until":null,"published":"2019-04-25","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: MuseNet\", as of 2019-04-25. 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MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony,"}},{"date":"2019-10-15","until":null,"published":"2019-10-15","recorded":"2026-11-03","citation":{"plain":"AsOf. \"OpenAI released: Automatic Domain Randomization\", as of 2019-10-15. Claim clm_6E175D93430F, https://asof.is/claim/clm_6E175D93430F, accessed 2026-11-13. 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