I was obsessed with getting an LLM model to solve a Rubik's Cube. It can't reason about space or time in any abstract way. For it to solve the puzzle, it would require training on millions of permutations in order for the weights to have been trained on every possible state. The most recent models can solve a Rubik's Cube people are saying -- I haven't tested it myself -- but that isn't because they are reasoning better, it would because they included millions of Rubik's Cube states with next moves as text in the training data, I presume.

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roenxi an hour ago | parent [-]

> but that isn't because they are reasoning better, it would because they included millions of Rubik's Cube states with next moves as text in the training data, I presume.

Isn't it far more likely that the LLM has memorised the well known algorithms for solving a Rubik's Cube and has become intelligent enough to execute them? That seems like it'd be a lot easier than memorising millions of cube states. It doesn't even seem obvious that it could memorise next moves, it seems [0] there are more possible states of the cube than these models have parameters. It'd need to be a Large Rubik's Cube Model (LRCM? LRM?) rather than an LLM.

[0] https://cube.alen.is/

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27 minutes ago | parent | next [-]

[deleted]

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danielbarla an hour ago | parent | prev | next [-]

Indeed, I suspect the approaches/algorithms for solving a Rubik's cube "compress" a lot better than trying to distill the entire search space in order to be able to predict the exact next move.

I see this trope fairly often, i.e. the assumption that an LLM would need to have been trained on <exact thing it is being asked to solve>. Now, while I do have a moderate amount of background in AI, I am definitely not an expert on LLMs as such. I would be interested to hear someone's take, who does work actively in LLM research. Can they generalise "well enough"? They certainly seem to be able to do so, from my anecdata, and I don't believe "training explicitly for every possible scenario" would have scaled even to today's state.

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datsci_est_2015 34 minutes ago | parent | prev [-]

Seems likeliest that it didn’t even “memorize” anything, in the anthropomorphic sense. The Rubik’s cube algorithm is trivially representable in code, as long as the interface for interacting with a cube is well-designed / well-defined.

I’m no more surprised that an LLM can solve a Rubik’s cube than it can send an HTTP request.

	▲	dataviz1000 21 minutes ago \| parent [-]
		Opus 4.6 can not solve a Rubik's Cube. What changed between Opus 4.6 and Fable and the GPT 5.6 models released since? LLM models cannot actually reason about a red or white piece sitting on the opposite side of the cube or figure out how to move it into place. The model knows where the piece is supposed to go because the algorithm tells it. What it cannot do is work out on its own which turns will get the piece there. The only way an LLM could solve this kind of problem is if it were trained on every possible arrangement of the cube ahead of time. Then it could simply output the matching text instructions it memorized instead of truly thinking through the moves. 3 months ago before the most advanced models could solve the cube, people on Hacker News kept saying that solving the Rubik's Cube with LLM is easy. I would love to see someone write a prompt using the best model at that time, Opus 4.6, that solves the cube! People are so sure of themselves without any evidence. It shows how much people idealize (that is probably the correct word) the AI. Of course, reinforcement learning can solve it which is what has happened on the latest models but so many people put blind faith into the AI. Here is just a small list of prompts I tried with Opus 4.6. [0] [0] https://github.com/adam-s/rubiks-cube/tree/main/prompts/vari...