An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments. Within the environment, the agent will perform a set of tasks that are represented as datasets. Therefore, environments can be split into broadly two parts: tasksets and runtimes. In this release, we are focusing on the tasksets.

An RL environment on the Hub is a dataset repo that shows up in the new RL Environments filter. The Use this dataset button gives you the command to run it in that framework. There is no new repo type, no registry, and no sign-up. There are already environments in Harbor, Verifiers, and NVIDIA NeMo Gym.

Every RL paper or framework uses its own way to find environments. Custom hubs, runtime registries, independent task datasets, or a GitHub list of tasks with a custom loader. This means that many of the published environments are siloed: if you publish an environment for one framework, users of the other three can’t load it. If you want to train on an environment from another framework or a new paper, you’ll need to port it by hand.

We think this is the wrong shape. An environment is tasks, tests, containers, and a reward rule, which are data with a runtime on top. The Hub already stores data, versions it, gates it, previews it, and serves it to millions of people. It does not need a second system to hold environments. It needs a way to say "this data is an environment, and here is how you run it."