In a more standard inference amortization setup one would e.g. train directly on question/answer pairs without the explicit reasoning path between the question and answer. In that way we pay an up-front cost during training to learn a “shortcut” between question and answers, and then we can use that pre-paid shortcut during inference. And we call that amortized inference.
Which sounds like supervised learning. Adam seemed to want to know how that relates to scaling up inference time compute so I said some ways they are related.
I don’t know much about amortized inference in general. The Goodman paper seems to be about saving compute by caching results between different queries. This could be applied to LLMs but I don’t know of it being applied. It seems like you and Adam like this “amortized inference” concept and I’m new to it so don’t have any relevant comments. (Yes I realize my name is on a paper talking about this but I actually didn’t remember the concept)
I don’t think I implied anything about o3 relating to parallel heuristics.
I was trying to say things related to this:
Which sounds like supervised learning. Adam seemed to want to know how that relates to scaling up inference time compute so I said some ways they are related.
I don’t know much about amortized inference in general. The Goodman paper seems to be about saving compute by caching results between different queries. This could be applied to LLMs but I don’t know of it being applied. It seems like you and Adam like this “amortized inference” concept and I’m new to it so don’t have any relevant comments. (Yes I realize my name is on a paper talking about this but I actually didn’t remember the concept)
I don’t think I implied anything about o3 relating to parallel heuristics.