Rumours say you do something like:

  Download every github repo
    -> Classify if it could be used as an env, and what types
      -> Issues and PRs are great for coding rl envs
      -> If the software has a UI, awesome, UI env
      -> If the software is a game, awesome, game env
      -> If the software has xyz, awesome, ...
    -> Do more detailed run checks, 
      -> Can it build
      -> Is it complex and/or distinct enough
      -> Can you verify if it reached some generated goal
      -> Can generated goals even be achieved
      -> Maybe some human review - maybe not
    -> Generate goals
      -> For a coding env you can imagine you may have a LLM introduce a new bug and can see that test cases now fail. Goal for model is now to fix it
    ... Do the rest of the normal RL env stuff

Every interactive system is a potential RL environment. Every CLI, every TUI, every GUI, every API. If you can programmatically take actions to get a result, and the actions are cheap, and the quality of the result can be measured automatically, you can set up an RL training loop and see whether the results get better over time.