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But this has only been shown on simple tasks, so I think this paper is still quite neat. The interesting thing is that they show "future horizon length" varies across models.
Confirmatory of Sutskever's view that predicting the next token forces a deep understanding. To effectively predict the next token it needs a good idea of what comes after the next token.
> To effectively predict the next token it needs a good idea of what comes after the next token. And that's all it needs. Not reasoning.
Babbage’s Analytical Engine didn’t actually analyze anything, and terminology hadn’t gotten any more clear-cut since.
How is this news? isn't it an obvious fact from the Transformer architecture?
Just below your question is a very confidently incorrect take about "parroting"... So, not obvious at all, at least for some people :)
> A coding agent solving a software-engineering task spends dozens of steps reasoning No. That's simple PR hype. Parrotry is not reasoning.
> A coding agent solving a software-engineering task spends dozens of steps reasoning No. That's simple PR hype. Parrotry is not reasoning.
Confirmatory of Sutskever's view that predicting the next token forces a deep understanding. To effectively predict the next token it needs a good idea of what comes after the next token.
> To effectively predict the next token it needs a good idea of what comes after the next token. And that's all it needs. Not reasoning.
> A coding agent solving a software-engineering task spends dozens of steps reasoning No. That's simple PR hype. Parrotry is not reasoning.
Then how do humans create something 'creative'—something that didn't exist before? I think it might be because the process of simplifying the complex system of nature differs between individuals. The data being learned now is all labeled by humans and simplified through human cognition. Within that kind of information, creativity seems hard to emerge.
Ultimately, with data that already contains interpretation, no matter how much you repeat the learning, it just becomes an encyclopedia that only explores within human knowledge, repeating predictions within human interpretation. So I wonder if we actually need a different encoder that interprets raw data—not based on human interpretation.
In reality, what changed Newton's absolute time to Einstein's relativity was a conclusion derived simply from observing the world. Newton's interpretation was supported by a lot of evidence in its time. If an AI studied all the medieval data from Newton's era, could it actually come up with the theory of relativity?
I'm always curious about this. I think AI is already very good at coding and will soon become better than humans. Logical structures are ultimately human interpretations, and reasoning within that framework is something AI can probably do more logically than humans. In other words, once humans create the framework, stacking the logical Jenga blocks within it—AI will be better at that.
But true creativity lies in breaking the framework itself, and I'm skeptical about whether AI can do that. The encoder also seems insufficient. There will likely be limits. I might be trapped in my own biases.
But the limitations of the current approach seem too clear to ignore.
When I look at the approach of these papers, it feels like an argument that adding shadows that imitate the world will eventually make them become the objects themselves.
I think the text, code, images, papers, and conversations that humans leave behind are not the world itself, but rather shadows of the world that have passed through human cognition and language. No matter how much you learn from those shadows, whether that leads to the ability to actually engage with the objects themselves seems like a separate issue.
I feel like something different is needed. But I'm not intellectually sharp enough to reason this through logically.this is just my intuition
Finally there is evidence that the model kinda actually knows the correct token spend on each method.