Right, but it leads to too many false conclusions by lay people. User facing LLMs are only trained on next token prediction during initial stages of their training. They have to go through Reinforcement Learning before they become useful to users, and RL training occurs on complete responses, not just token-by-token.

That leads to conclusions elucidated by the very article, that LLMs couldn't possibly plan ahead because they are only trained to predict next tokens. When the opposite conclusion would be more common if it was better understood that they go through RL.