The kind of stuff that people are doing with decision models today is largely the way I've been using Liquid AI's LFM models, as rapid iteration interrogation tools for atomic decisions. They aren't good at reasoning, but you can reason about what decisions you would need in order to come to a reasonably high quality decision about a thing.
Even though they weren't themselves decision models exactly, they were so fast that you could use them in a similar way. They obliterated Qwen and other models for a lot of tasks in that use case.
I'm not sure I would agree with some of the claims made in this article though. You can definitely do a lot of similar tasks to regular LLMs with decision models, you just have to chain the results. You can even technically infuse a broader perspective into each token choice or force certain context to have priority in the decision making of the next token.
Even though they weren't themselves decision models exactly, they were so fast that you could use them in a similar way. They obliterated Qwen and other models for a lot of tasks in that use case.
I'm not sure I would agree with some of the claims made in this article though. You can definitely do a lot of similar tasks to regular LLMs with decision models, you just have to chain the results. You can even technically infuse a broader perspective into each token choice or force certain context to have priority in the decision making of the next token.