Google published a very interesting semi-philosophical study about What rizoning actually is, write that RL, in fact, teaches models to think not longer, but more collectively.
🟢 What Google observed?
You've surely noticed that when a model thinks, it often simulates a dialogue between different internal voices. It asks itself questions, may criticize or highlight something. And Google writes that the phenomenon of rizoning is contained in this structure of internal dialogue.
The most interesting thing is HOW THEY PROVE IT?
- The authors take a sparse autoencoder (what it is and why it's needed we wrote here) and find a neural feature that is responsible for surprise/awareness/change of viewpoint. This feature is activated at the beginning of sentences in dialogue contexts, and in practice, it's simply responsible for the use of things like "Oh!", "Wait a minute", "Oh, so...".
- Then this feature is specially strengthened during generation and the metrics are observed (model - DeepSeek-R1-Llama-8B).
- RESULT: on complex combinatorial arithmetic tasks, where the original model gives 27.1% accuracy, the model with strengthened dialogue marker already gives 54.8%, and with suppression of this marker - 23.8%.
The statistical significance has been checked: the authors specifically compared the strengthening of this feature with the strengthening of other features, and the effect is obvious. Plus, in parallel with the strengthening of this marker in the model, the ability for cognitive strategic thinking also increases.
In short, LLMs are still only 0.01% understood. We need to somehow try to write in the prompt Use more "ah", "oh", "exactly" and "oh yeah", and observe result. Paper
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