What are the state-of-the-art results in OpenAI's gym environments?

What are the state-of-the-art results in OpenAI's gym environments? Is there a link to a paper/article that describes them and how these SOTA results were calculated?

There is the leaderboard page at the gym GitHub repository that contains links to specific implementations that "solve" the different gym environments, where "to solve" means "to reach a certain level of performance", which, given a fixed reward function, is typically measured as the average (episodic) return/reward. For example, in the case of the CartPole environment, you solve it when you get an average reward of $$195.0$$ over $$100$$ consecutive trials.