Where the term appears in this channel In this channel's material, RL environments show up in a conversation about how frontier models get trained. Edwin Chen, founder of Surge AI, frames model training as a school for AI — and inside that framing, environments are described as replacing static datasets. Instead of handing a model a fixed pile of examples, an RL environment gives it a setting to act in and get feedback from, so training moves from “here are the answers” toward “here is a situation — try things and see what works.” Why the shift matters The handout for that conversation puts it directly: explore from static datasets to RL environments as one of the key movements in modern training infrastructure. The idea pairs with the broader argument in the channel's material that the form factor of training data is changing — not just more data, but more interactive, feedback-driven data. - RL environments = interactive training settings where a model acts and receives feedback, rather than consuming a fixed dataset. - In this channel, they are presented as replacing static datasets in frontier model training. - They fit Edwin Chen's “school for AI” framing: the environment is the classroom, not the textbook.