openai/gym
A toolkit for developing and comparing reinforcement learning algorithms.
Whisper, Triton, Evals, Swarm, and API tooling
A toolkit for developing and comparing reinforcement learning algorithms.
gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
MLE-bench is a benchmark for measuring how well AI agents perform at machine learning engineering
A lightweight, powerful framework for multi-agent workflows and voice agents
An official OpenAI toolkit for social scientists and data scientists to measure quantitative attributes in text, images, or audio using the GPT API.
Code for the paper "Jukebox: A Generative Model for Music"
Code for the paper "Language Models are Unsupervised Multitask Learners"
A lightweight, powerful framework for multi-agent workflows
An educational resource to help anyone learn deep reinforcement learning.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Renderer for the harmony response format to be used with gpt-oss
Code for the paper Fine-Tuning Language Models from Human Preferences
Code for the paper "Quantifying Transfer in Reinforcement Learning"
Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"
Code and model for the paper "Improving Language Understanding by Generative Pre-Training"
A living collection of deep learning problems
Code for the paper "Evaluating Large Language Models Trained on Code"
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Dataset of GPT-2 outputs for research in detection, biases, and more
Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
GPT-5 coding examples
Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"
Code for the paper "Generative Adversarial Imitation Learning"
Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
GPT-3: Language Models are Few-Shot Learners