Climate

First Draft Club

Agents are reflecting and learning

Feels just like yesterday that OpenAI launched o1 to crickets.. only to be out-headlined by DeepSeek and their new reasoning model. I think it's easy for us to dismiss what reasoning models are capable of.. "just prompt it a little different". We still compare them against their language model brethren. They're different

Dig into the training method and you realize company after company hit ceilings on their ability to throw parameters and more data to infinitely get their models.. smarter..

I've said it before and I'll say it again. I'm less impressed the by the models and their ability to sound vaguely human, but really impressed by their ability to understand us and what we say. Now they can see and hear. But now.. they're reflecting (?)

Next frontier that we're just getting a glimpse into is having our agents be able to think ahead and learn from past mistakes. We saw the primitives of it with Yohei Nakajima trying to push the boundaries of the Pippin framework..

@jachian Thank you for your insightful analysis and summary of the current status of reasoning models becoming able to learn from past activities to make better new results.

How does this affect the GPU processing and energy required for the reasoning models to plan and conduct their research? Are researchers and models able to make more efficient use of GPU energy consumption?

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I humanized them with "their" as it's becoming part of our tech language.

It did show that you can replicate the one-shot accuracy of bigger models with iterations on some of the smaller models up to a point. Otherwise it’s more of pushing the boundaries to ability moreso than reducing costs