Curious about the training data of OpenAI's new GPT-OSS models? I was too

I mean you can go read his blog post that's pinned where he argues that there's no new ideas and it is all data. He makes the argument that architecture doesn't matter, which is just so demonstrably false that it is laughable. He's a scale maximalist.

I also expect an AI researcher from a top university to not make such wild mistakes

  > 3. RLHF: first proposed (to my knowledge) in the InstructGPT paper from OpenAI in 2022

  | Specifically, we use reinforcement learning from human feedback (RLHF; Christiano et al., 2017; Stiennon et al., 2020) to fine-tune GPT-3 to follow a broad class of written instructions (see Figure 2).

  || Our algorithm follows the same basic approach as Akrour et al. (2012) and Akrour et al. (2014)

I'm sure we can trace the idea back to the 80's if not earlier. This is the kind of take I'd expect a non-researcher to have, but not someone with two dozen NLP papers. The Instruct-GPT paper was just the first time someone integrated RLHF into a LLM (but not a LM).

Maybe a better article is the one he wrote on Super Intelligence From First Principles. As usual, when someone says "First Principles" you bet they're not gonna start from First Principles... I guess this makes sense in CS since we index from 0

[0] https://arxiv.org/abs/2203.02155

[Christiano et al] https://arxiv.org/abs/1706.03741

[Stiennon eta al] https://arxiv.org/abs/2009.01325

[Akrour et al (2012)] https://arxiv.org/abs/1208.0984

  > Personally, I expect more rigor from any analysis and would hold myself to a higher standard.

It worries me that I get this feeling from quite a number of ML people who are being hired and paid big bucks from big tech companies. I say this as someone in ML too. There's a propensity to just accept outputs rather than question them. This is like a basic part of doing any research, you should always be incredibly suspicious of your own results. What did Feynman say? Something like "The first rule is not to be fooled and you're the easiest person to fool"?

Feeding an empty prompt to a model can be quite revealing on what data it was trained on

It's an open model, everyone can bench it on everything not only on specific benchmarks. Training on specific reasoning benchmarks is a conjecture.

EDIT: orbital decay explained it pretty well in this thread

it originally referred to the idea that neural signals might form an intrinsic "language" representing aspects of the world, though these signals gain meaning only through interpretation in context.

in artificial intelligence, the term now has a more concrete role, referring to the deep communication protocols used by multiagent systems.

One implication leading to its popularity by LessWrong is the worry that malicious AI agents might hide bad intent and actions by communicating in a dense, indecipherable way while presenting only normal intent and actions in their natural language output.

https://en.wiktionary.org/wiki/mentalese

EDIT: After reading the original thread in more detail, I think some of the sibling comments are more accurate. In this case, neuralese is more like language of communication expressed by neural networks, rather than its internal representation.

1) internally, in latent space, LLMs use what is effectively a language, but all the words are written on top of each other instead of separately, and if you decode it as letters, it sounds like gibberish, even though it isn't. It's just a much denser language than any human language. This makes them unreadable ... and thus "hides the intentions of the LLM", if you want to make it sound dramatic and evil. But yeah, we don't know what the intermediate thoughts of an LLM sound like.

The decoded version is often referred to as "neuralese".

2) if 2 LLMs with sufficiently similar latent space communicate with each other (same model), it has often been observed that they switch to "gibberish" BUT when tested they are clearly still passing meaningful information to one another. One assumes they are using tokens more efficiently to get the latent space information to a specific point, rather than bothering with words (think of it like this: the thoughts of an LLM are a 3d point (in reality 2000d, but ...). Every token/letter is a 3d vector (meaning you add them), chosen so words add up to the thought that is their meaning. But when outputting text why bother with words? You can reach any thought/meaning by combining vectors, just find the letter moving the most in the right direction. Much faster)

Btw: some specific humans (usually toddlers or children that are related) when talking to each other switch to talking gibberish to each other as well while communicating. This is especially often observed in children that initially learn language together. Might be the same thing.

These languages are called "neuralese".

That's the nature of reinforcement learning and any evolutionary processes. That's why the chain of thought in reasoning models is much less useful for debugging than it seems, even if the chain was guided by the reward model or finetuning.