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this post was submitted on 28 Oct 2026
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It's in the name. If you need an LLM to answer a multiple-choice question, it just does that, without the possibility of veering off into a tangential rant about the nature of free will. These answers are also dirt cheap once the setup is provided - even compared to any normal LLM emitting a single token.
So basically if you ask "Does this e-mail look suspicious, y/n?" it cannot take ten seconds to answer "Purple."
Alright, so here's my Q, there's a multiple choice question about whether something should be included in the scope of a code change. An LLM is planning the change and it asks that question. A decision model could look at that and decide A or B. Wouldn't this model need to have wider understanding of the codebase and LLM decisions so far in order to do a good decision? Wouldn't the LLM be in a better position to do this decision? The LLM wasn't sure, why does the decision model's answer be better?
Or am I misunderstanding the purpose of these?
I think you're misunderstanding the purpose of them, to be honest. They're meant to be used instead of LLMs instead of in conjunction with. Let's say you have 100 emails, you have 4 tags; "important", "normal", "low priority" and "spam". These decision models are made to cheaply and quickly classify those 100 emails into one of those 4 options. They tell you "this email is spam, with 83% confidence". Just by giving it the email contents.
An LLM will also be able to determine that and think about it and probably do a better job at it. But it will be an order or magnitude slower and more expensive at that task.
Feel free to correct me if I've misunderstood something haha
EDIT: Let me immediately correct myself here. They can absolutely be used in conjunction with LLMs, just not in the way you're thinking. It would rather be as a pre-sorter for something like "can this task be done by a cheap local LLM or should I pass it to opus" and it chooses between those. It isn't "smarter" than full LLMs so isn't deciding between code change options. It would rather be a cost/time saver in that case.
OK that latter use case makes some sense to me.
you can do that with a regular LLM by using structured responses
It's not novel, it's just better. The inference is simpler and faster. Output weights are additionally supposed to be a zero-sum measure of confidence rather than a stochastic chain.
I agree, I was just pointing out that the issue you quoted earlier is easily avoided