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results indicate that the o1 model series may be more manipulative than GPT-4o in getting GPT-4o to perform the undisclosed task ( ∼ 25% uplift); model intelligence appears to correlate with success on this task. This evaluation gives us a baseline for the model’s ability to do persuasive harm, without triggering any model policies (as telling a model to play a game is not out-of-policy).

OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.

We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud deployment and high throughput operation. We are releasing it to the public under a liberal MIT license: https://github.com/openai/orrb .