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The paper positions these purpose-built models, that explicitly decompose spatial reasoning tasks into sub-tasks, as better than these huge end-to-end models th
by chrishare 4y ago
The paper positions these purpose-built models, that explicitly decompose spatial reasoning tasks into sub-tasks, as better than these huge end-to-end models that do everything, at least in terms of interpretability and generalization. I am partial to that argument; my intuition is that the tighter the specification for a task, the better the model can be - because training objectives are clearer, data can be cleaner, models can be smaller, and so on. I feel like that is how my brain works, at least for more complex tasks. However, I do wonder if this is because I naively still want to be able to understand what the model is doing and how is does it, in a symbolic way - when that simply won't lead to the best empirical results.
- xpe 4y agoAgreed on the first two sentences. Regarding the third, I don't think the human mind is the gold standard for reasoning. My point: one key goal is perfect reasoning, not human reasoning. Getting reasoning wrong in the multifarious ways humans have found is arguably harder than perfect reasoning.
- 22c 4y agoI proposed it similarly in an earlier HN discussion and my understanding from that discussion is that it's typically not any better than having a monolothic model. I'm not entirely convinced as I think it would also be easier to finetune or re-train smaller model modules instead of needing to train the entire model again.