Thanks to Ian McKenzie and Nicholas Dupuis, collaborators on a related project, for contributing to the ideas and experiments discussed in this post. Ian performed some of the random number experiments.

Also thanks to Connor Leahy for feedback on a draft, and thanks to Evan Hubinger, Connor Leahy, Beren Millidge, Ethan Perez, Tomek Korbak, Garrett Baker, Leo Gao and various others at Conjecture, Anthropic, and OpenAI for useful discussions.

This work was carried out while at Conjecture.

Important correction

I have received evidence from multiple credible sources that text-davinci-002 was not trained with RLHF.

The rest of this post has not been corrected to reflect this update. Not much besides the title (formerly "Mysteries of mode collapse due to RLHF") is affected: just mentally substitute "mystery method" every time "RLHF" is invoked as the training method of text-davinci-002. The observations of its behavior otherwise stand alone.

This is kind of fascinating from an epistemological standpoint. I was quite surprised to learn that text-davinci-002 was probably not trained with RLHF. I don't remember exactly how "text-davinci-002 is RLHF" got elevated to an unquestioned assumption in my mind. I might have mistook not being contradicted by people who I assumed were in the know as confirmation. I certainly did not expect to talk for months to dozens of people about odd behaviors I've observed in a well-known model "due to RLHF" without being contradicted in a world where the model in question wasn't trained with RLHF, but that's what happened.^[1] It wasn't just me either: the assumption that text-davinci-002(/text-davinci-001) is InstructGPT is RLHF seems ambient (e.g. search "text-davinci-002 rlhf" on Twitter, this LW post, this article, and many others). I contributed to perpetuating this misinformation cascade, and for that I apologize.

text-davinci-002's behaviors described in this post also contributed to my confidence because RLHF seemed to be a likely and potentially satisfying explanation. Its apparently unsubstantiated confidence in very specific outcomes seems antithetical to the outer objective of self-supervised learning, which is optimized by epistemic calibration, meaning the model's entropy should be as high as possible while fitting the data. In contrast, as several comments have pointed out, it makes sense that RL kills entropy. The presence o...

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