As a Prolog enthusiast and someone who had followed Newell & Simon's SOAR work etc., this was intriguing. There are some respositories that seem related:
https://github.com/pcoz/operational-representation-of-system...
But, it is disappointing that the summary does seem to be AI-generated slop and I can't find code reproducing the claims as described.
> A large language model is trained to predict the next word of a piece of text. … simply asking a model to “think step by step” makes its answers better.
I thought both these statements were no longer held to be broadly true? At least in big lab or frontier models.
Next token prediction is still how pre-training works, but that's a relatively small part of the whole training run nowadays. Most of the time is spent in post-training reinforcement learning scenarios, where models are scored based on an evaluation of their ability to complete a task, not whether any specific token correctly predicts a known next token.
Generally, anyone talking about next token prediction being some limiting factor of LLM capability is a good reason to ignore them, as they haven't updated their understanding of LLMs since 2023 or so.
The method could not be performed on the resource because the server is unable to store the representation needed to successfully complete the request. There is insufficient free space left in your storage allocation.
Additionally, a 507 Insufficient Storage error was encountered while trying to use an ErrorDocument to handle the request."
It's really a nitpick but putting a big, obvious AI generated slop photo at the top of your post is a signal for bad quality content. I would recommend to avoid doing it.
I wouldn't even mind it if it weren't so blatantly lazy (lazier). It's always that off-putting overly busy default look. Like, you couldn't even be bothered to ask for some refinement.
If it's really such a small model why not provide a web interface where we can test it for ourselves?
This feels like high risk of being crackpottery.
I can also find no working model. I expect this may be disregarded for now.
I thought both these statements were no longer held to be broadly true? At least in big lab or frontier models.
Generally, anyone talking about next token prediction being some limiting factor of LLM capability is a good reason to ignore them, as they haven't updated their understanding of LLMs since 2023 or so.
Only if you call 90% small.
No-one is claiming <50%.
Although that's arguably way superior to AI-style reasoning.
"Insufficient Storage
The method could not be performed on the resource because the server is unable to store the representation needed to successfully complete the request. There is insufficient free space left in your storage allocation.
Additionally, a 507 Insufficient Storage error was encountered while trying to use an ErrorDocument to handle the request."
2. Obtain an OpenAI API Key
(I mean, is he grading the dive?)