It makes intuitive sense. How else could you write a 500-line script top-to-bottom with no backspace key and no arrow keys and get all the imports etc. right upfront?
...by inferring both the imports and the script body from the same context? I think you're suggesting there's some kind of information flow from the anticipated body of the script back up to the imports, but I don't see why that would be necessary. Infer imports from context, infer body from context + imports. All strictly causal.
Sure, try it. It's harder than you think. It's not just imports, it's the entire program.
> you're suggesting there's some kind of information flow from the anticipated body of the script back up to the imports
Yes, I am suggesting this. I don't think it is possible to write programs without either anticipating what you're going to write down below before you get there, or else being able to go back and edit what you already wrote.
Of course agent harnesses allow the latter, but raw models without a harness can still do an exceptionally, superhumanly, good job of straight-line programming with no editing.
> Infer imports from context, infer body from context + imports. All strictly causal.
Of course it's causal, that's kind of a reductive way to look at it.
Just infer the entire program from context and then type what you inferred.
That suggests a straightforward mapping between context -> program. Maybe for trivial programs but I don’t see how you can argue that for sufficiently complex programs.
Reasoning models perform better than non-reasoning models because they’re able to refine their code in multiple steps. That allows any part of the program to influence any other part of the program, not just from start -> end.
Human thinking serves a similar purpose. Basically intelligence needs to be able to backtrack if you want better performance.
Right, reasoning models are almost more notable for what they don't do, which is: try to generate the first token of the response from a single forward pass through the weights.
Non-reasoning models were still surprisingly good at generating working programs. I think it makes sense when you consider that it's still iterative; it doesn't have to generate the entire import block in a single pass, for example, more like decide at the end of each line whether there are more libraries it should import or whether to move on to the body of the program. It's less surprising that a single forward pass is able to make that type of decision IMO.
Taking Python as an example, you see LLMs use a lot of scoped imports and I think this is partly because it makes the output more resilient against failure to think forward at the start. Even a reasoning model will write out (the first iteration of) an entire file in one tool call, so once it starts generating it still has to keep going until it finishes, no pausing for breath.
Also it's interesting that non-reasoning models can still do quite well in a harness that lets them test and fix up their code. Like you said, it gives them the ability to iterate.
> you see LLMs use a lot of scoped imports and I think this is partly because it makes the output more resilient against failure to think forward at the start.
What makes you think an LLM has any such motivation? Or any motivation at all - beyond generating the most likely next token?
The fact that RLVR rewards successful trajectories.
Notice I didn't use the word motivation, which you decided to use. The reason LLMs use a technique is simply because it works, and that comes straight from training (specifically RLVR post-training).