> Finally, we've introduced thinking summaries for Claude 4 models that use a smaller model to condense lengthy thought processes. This summarization is only needed about 5% of the time—most thought processes are short enough to display in full. Users requiring raw chains of thought for advanced prompt engineering can contact sales about our new Developer Mode to retain full access. I don't want to see a "summary" of…
There are several papers pointing towards 'thinking' output is meaningless to the final output, and using dots, or pause tokens enabling the same additional rounds of throughput result in similar improvements. So in a lot of regards the 'thinking' is mostly marketing. - "Think before you speak: Training Language Models With Pause Tokens" - https://arxiv.org/abs/2310.02226 - "Let's Think Dot by Dot: Hidden Computation…
So, to anyone more knowledgeable than the proprietor of that channel: can you outline why it's possible to replace thinking tokens with garbage without a decline in output quality?
edit: Section J of the first paper seems to offer some succint explanations.