You don't need RL for the conclusion "trained to predict next token => only things one token ahead" to be wrong. After all, the LLM is predicting that next toke

You don't need RL for the conclusion "trained to predict next token => only things one token ahead" to be wrong. After all, the LLM is predicting that next token from something - a context, that's many tokens long. Human text isn't arbitrary and random, there are statistical patterns in our speech, writing, thinking, that span words, sentences, paragraphs - and even for next token prediction, predicting correctly means learning those same patterns. It's not hard to imagine the model generating token N is already thinking about tokens N+1 thru N+100, by virtue of statistical patterns of preceding hundred tokens changing with each subsequent token choice.