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| 2019 || {{dts|December 3}} || Software release || OpenAI releases Procgen Benchmark, a set of 16 simple-to-use procedurally-generated environments (CoinRun, StarPilot, CaveFlyer, Dodgeball, FruitBot, Chaser, Miner, Jumper, Leaper, Maze, BigFish, Heist, Climber, Plunder, Ninja, and BossFight) which provide a direct measure of how quickly a {{w|reinforcement learning}} agent learns generalizable skills. Procgen Benchmark prevents AI model overfitting.<ref>{{cite web |title=Procgen Benchmark |url=https://openai.com/blog/procgen-benchmark/ |website=openai.com |accessdate=2 March 2020}}</ref><ref>{{cite web |title=OpenAI’s Procgen Benchmark prevents AI model overfitting |url=https://venturebeat.com/2019/12/03/openais-procgen-benchmark-overfitting/ |website=venturebeat.com |accessdate=2 March 2020}}</ref><ref>{{cite web |title=GENERALIZATION IN REINFORCEMENT LEARNING – EXPLORATION VS EXPLOITATION |url=https://analyticsindiamag.com/generalization-in-reinforcement-learning-exploration-vs-exploitation/ |website=analyticsindiamag.com |accessdate=2 March 2020}}</ref>
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| 2019 || {{dts|December 4}} || Publication || "Deep Double Descent: Where Bigger Models and More Data Hurt" is submitted to the {{w|ArXiv}}. The paper shows that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as the model size increases, performance first gets worse and then gets better.<ref>{{cite web |last1=Nakkiran |first1=Preetum |last2=Kaplun |first2=Gal |last3=Bansal |first3=Yamini |last4=Yang |first4=Tristan |last5=Barak |first5=Boaz |last6=Sutskever |first6=Ilya |title=Deep Double Descent: Where Bigger Models and More Data Hurt |website=arxiv.org |url=https://arxiv.org/abs/1912.02292|accessdate=5 April 2020}}</ref>
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| 2019 || {{dts|December}} || Staff || Dario Amodei is promoted as OpenAI's Vice President of Research.<ref name="Dario Amodeiy">{{cite web |title=Dario Amodei |url=https://www.linkedin.com/in/dario-amodei-3934934/ |website=linkedin.com |accessdate=29 February 2020}}</ref>
