Isn't the basic idea of Constitutional AI just having the AI provide its own training feedback using written instruction? My guess is there was a substantial amount of self-evaluation in the o1 training with complicated written instructions, probably kind of similar to a constituion (though this is just a guess).

As a tangent to my question, I wonder how many AI companies are already using RLAIF and not even aware of it. From a recent WSJ story:

Early last year, Meta Platforms asked the startup to create 27,000 question-and-answer pairs to help train its AI chatbots on Instagram and Facebook.

When Meta researchers received the data, they spotted something odd. Many answers sounded the same, or began with the phrase “as an AI language model…” It turns out the contractors had used ChatGPT to write-up their responses—a complete violation of Scale’s raison d’être.

So they detected the cheating that time, but in RLHF how would they know if contractors used AI to select which of two AI responses is more preferred?

BTW here's a poem(?) I wrote for Twitter, actually before coming across the above story:

The people try to align the board. The board tries to align the CEO. The CEO tries to align the managers. The managers try to align the employees. The employees try to align the contractors. The contractors sneak the work off to the AI. The AI tries to align the AI.

These posts might be relevant:

• A recipe for frontier model post-training
• Futures of the data foundry business model

The details of Constitutional AI seem highly contingent, while the general idea is simply automation of data for post-training, so that the remaining external input is the "constitution". In the original paper there are recipes both for instruction tuning data and for preference data. RLAIF is essentially RLHF that runs on synthetic preference data, maybe together with a recipe for generating it. But preference data could also be used to run DPO or something else, in which case RLAIF becomes a misnomer for describing automation of that preference data.

Llama 3 report suggests that instruction tuning data can be largely automated, but human preference data is still better. And data foundry business is still alive, so a lot of human data is at least not widely recognized as useless. But it's unclear if future models won't soon do better than humans at labeling, or possibly already do better at some leading labs. Meta didn't have a GPT-4 level model as a starting point before Llama 3, and then there are the upcoming 5e26 FLOPs models, and o1-like reasoning models.