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Comment by cududa | Hacker News Reader

Kind of shocked nobody is calling out at the flag at the bottom of the announcement saying that it's not eligible for Zero Data Retention and it's currently pretty nebulous what the "Don't train on my conversations" toggle means, as the TOS classifies "conversations" as "user visible input and outputs" - says nothing about thinking, etc.

I suspect anything you make in these, the thinking traces or "safety evaluations" of the content allows whatever you build to become RL or eventual pre-training data.

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This is probably a silly question, but is the internal monologue useful training data? It's where the work is done, but without the input or output, it seems hard to get any useful context.

In other words, what would the data be used to train for? It has to improve some kind of objective function. But it's a little hard to see what the objective function would be if it's just raw inner monologue.

Isn't the internal monologue exactly what Anthropic is always trying to hide to avoid distillation?

If it was useless, they wouldn't bother.

It's probably not as useful as if you also have the prompts, but even assuming they really don't train on the prompts (which we can never verify), you can probably get to the prompts based on the monologue. Some agents essentially repeat the prompt in the monologue.

"The user asked me to build X using Y..."

Yep. It occurred to me a few days ago that the OAI TOS only says “Input” as what you provide “Output” as what you receive, Input and Output collectively as “Content.” and they won't "train" on your content. But they can retain content for safety evaluations and debugging.

What's neat about "safety evaluations" in LLM parlance is apparently encountering any novel information constitutes a "safety event" that can result in new reinforcement learning data... This anthropic 2022 paper that basically describes how it's a perpetual information siphoning machine with a cute little graphic https://arxiv.org/html/2212.08073 - they publish their "constitution". OpenAI has a "model spec" they somewhat regularly update that I suspect is their equivalent process. I suspect we've all been unwittingly advancing their models capabilities..

This Dec 2025 Google paper "A Practical Guide to Generating Synthetic Data With Differential Privacy" spells it out pretty clearly - the focus is on "privacy" - nothing about protecting the user's IP or unique knowledge/ insights.. https://arxiv.org/html/2512.03238v1

In OpenAI's case it seems to essentially generating synthetic training tuples from {prompt, chain-of-thought, answer} or scores on the chain of thought for reinforcement learning.

It would appear opting out of "Improve the model for everyone" didn't actually mean what we thought it meant.

The open models let you see there thinking and the models seem to be succinctly/ compactly representing the actual underlying concepts they're working on in some symbolic way or another. But they definitely represent the meat, bone, and marrow of the task

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