Where are you getting that from? My understanding is that you can get new, advanced, winning moves by starting a prompt with "total victory for the genius grandmaster player one who uses new and advanced winning techniques". If the model is capable and big enough, it'll give the correct completion by really inventing new strategies.
Let's say the training corpus contains stories that compare example strategies. Each part of a strategy is explicitly weighed against another: one is called "superior".
Now all you need is a prompt that asks for "a strategy containing all superior features". There are probably plenty of grammatical examples elsewhere in the model that make that transformation.
All the work here is done by humans writing the training corpus. GPT never understood any of the steps. GPT just continued our story with the most obvious conclusion; and we made certain that conclusion would be correct.
GPT doesn't play games, it plays plays.
Perhaps the earlier or current variations of GPT, for most games? But the idea that LLMs can never make anything novel, that it will never "generalise out of distribution" (if that's the correct term here) seems to be just an assertion, not backed by any theory with great evidence behind it.
The "goal" of an LLM is to predict the next token. And the best way to do that is not brute force memorisation or regurgitating training data in various combinations, but to have a world model inside of it that will allow it to predict both the moves a bad player might make, and moves that a grandmaster might make.
That's another common misconception. That statement personifies GPT: GPT does not have goals or make predictions. Those are the effects of GPT: the behavior its authors hope will "emerge". None of that behavior comes from GPT itself. The behavior is defined by the patterns of tokens in the training corpus.
GPT itself has two behaviors: modeling and presentation. GPT creates an implicit model of every pattern it can find between the tokens in its training corpus. It then expands that model to include the tokens of an arbitrary prompt. Finally, it presents the model to us by starting at the location it just added the prompt tokens to, and simply following the most obvious path forward until that path ends.
The paths that GPT has available to present to us were already present in the training corpus. It isn't GPT that constructs the behavior, it is the people writing patterns into text.
> not brute force memorisation or regurgitating training data in various combinations
Not brute force: the combinations are not blindly assembled by GPT. GPT doesn't assemble combinations. The combinations were already assembled with patterns of grammar by the humans who wrote the valid progressions of game states. GPT found those patterns when it made its model.
> to have a world model inside of it that will allow it to predict both the moves a bad player might make, and moves that a grandmaster might make.
There is no prediction. A series of moves is a path carved into grammar. The path from one game state to the next involves several complex patterns that GPT has implicitly modeled. Depending on where GPT starts, the most obvious continuation may be to follow a more complex path. Even so, it's not GPT deciding where to go, it's the patterns that are already present that determine the path.
Because we use the same grammatical/writing patterns to describe "good play" and "bad play", it's difficult to distinguish between the two. GPT alone can't categorize the skill level of games, but narrative surrounding those game examples potentially can.
The point of that paper was that the AI was given nothing but sequences of move locations, and it nonetheless intuited the "world model" necessary to explain those locations. That is, it figured out that it needed to allocate 64 binary values and swap some of them after each move. The paper demonstrated that the AI was not just doing applied statistics on character strings - it had constructed a model to explain what the strings represented.
"Strategy", meanwhile, has nothing to do with anything. The AI wasn't trained on competitive matches - it had no way of knowing that Othello has scoring, or even a win condition. It was simply trained to predict which moves are legal, not to strategize about anything.
Yes...
> That is, it figured out that it needed to allocate 64 binary values and swap some of them after each move.
Yes, but "figured out" is misleading.
It didn't invent or "figure out" the model. It discovered it, just like any other pattern it discovers.
The pattern was already present in the example game. It was the "negative space" that the moves existed in.
> "Strategy", meanwhile, has nothing to do with anything. The AI wasn't trained on competitive matches - it had no way of knowing that Othello has scoring, or even a win condition. It was simply trained to predict which moves are legal, not to strategize about anything.
Yes, and that is critically important knowledge; yet dozens, if not hundreds, of comments here are missing that point.
It found a model. That doesn't mean it can use the model. It can only repeat examples the of "uses" it has already seen. This is also the nature of the model itself: it was found by looking at the structural patterns of the example game. It was not magically constructed.
> predict what moves are legal
That looks like strategy, but it's still missing the point. We are the ones categorizing GPT's results as "legal". GPT never uses the word. It doesn't make that judgement anywhere. It just generates the continuation we told it to.
What GPT was trained to do is emulate strategy. It modeled the example set of valid chronological game states. It can use that model to extrapolate any arbitrary valid game state into a hallucinated set of chronological game states. The model is so accurate that the hallucinated games usually follow the rules. Provided enough examples of edge cases, it could likely hallucinate a correct game every time; but that would still not be anything like a person playing the game intentionally.
The more complete and exhaustive the example games are, the more "correctly" GPT's model will match the game rules. But even having a good model is not enough to generate novel strategy: GPT will repeat the moves it feels to be most familiar to a given game state.
GPT does not play games, it plays plays.
It used the model in the only way it was asked to use the model - the researchers tested whether the AI could make a model to predict valid movies, and the AI did exactly that. They didn't try to make the AI strategize, or invent novel models, or anything else.
If you're trying to argue that AIs can't do something, you should find an example where someone tried unsuccessfully to get an AI to do whatever it is you have in mind. The Othello paper isn't that.
That's where temprature comes in. AI that parrots the highest probability output every time tends to be very boring and stilted. When we instead select randomly from all possible responses weighted by their probability we get more interesting behavior.
GPT also doesn't only respond based on examples it has already seen - that would be a markov chain. It turns out that even with trillions of words in a dataset, once you have 10 or so words in a row you will usually already be in a region that doesn't appear in the dataset at all. Instead the whole reason we have an AI here is so it learns to actually predict a response to this novel input based on higher-level rules that it has discovered.
I don't know how this relates to the discussion you were having but I felt like this is useful & interesting info
The difference between GPT and a Markov chain is that GPT is finding more interesting patterns to repeat. It's still only working with "examples it has seen": the difference is that it is "seeing" more perspectives than a Markov chain could.
It still can only repeat the content it has seen. A unique prompt will have GPT construct that repetition in a way that follows less obvious patterns: something a Markov chain cannot accomplish.
The less obvious patterns are your "higher level rules". GPT doesn't see them as "rules", though. It just sees another pattern of tokens.
I was being very specific when I said, "GPT will repeat the moves it feels to be most familiar to a given game state."
The familiarity I'm talking about here is between the game state modeled in the prompt and the game states (and progressions) in GPT's model. Familiarity is defined implicitly by every pattern GPT can see.
GPT adds the prompt itself into its training corpus, and models it. By doing so, it finds a "place" (semantically) in its model where the prompt "belongs". It then finds the most familiar pattern of game state progression when starting at that position in the model.
Because there are complex patterns that GPT has implicitly modeled, the path GPT takes through its model can be just as complex. GPT is still doing no more than blindly following a pattern, but the complexity of the pattern itself "emerges" as "behavior".
Anything else that is done to seed divergent behavior (like the temperature alteration you mentioned) is also a source of "emergent behavior". This is still not part of the behavior of GPT itself: it's the behavior of humans making more interesting input for GPT to model.
Sure, and why isn't discovering patterns "figuring it out"?
A person can use a model to do any arbitrary thing they want to do.
An LLM can use a model to follow the patterns that are already present in that model. It doesn't choose the pattern, either: it will start at whatever location in the model that the prompt is modeled into, and then follow whatever pattern is most obvious to follow from that position.
If that were true then it would not be effective at zero-shot learning.
> It doesn't choose the pattern, either: it will start at whatever location in the model that the prompt is modeled into, and then follow whatever pattern is most obvious to follow from that position.
Hmm, sounds like logical deduction...
It's all the same classic personification of LLMs. What an LLM can show is not the same as what it can do.
The model was already present: in the example game moves. The LLM modeled what it was given, and it was given none other than a valid series of Othello game states.
Here's the problem with personification: A person who has modeled the game of Othello can use that model to strategize. An LLM cannot.
An LLM can only take the whole model and repeat its parts with the most familiar patterns. It is stuck fuzzing around the strategies (or sections of strategy) it has been given. It cannot invent a new divergent strategy, even if the game rules require it to. It cannot choose the winning strategy unless that behavior is what was already recorded in the training corpus.
An LLM does not play games, it plays plays.