Reminder that there are degrees of "trained on conversations". From John Schulman:

> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper

> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this

> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"

source: https://x.com/johnschulman2/status/2097440545853637108