Actually now that I think about it, LLM's are decoder only these days. But decoders and encoders are architecturally very similar. You could probably cut off the "head" of the decoder, make a few fully connected layers, and fine tune them to provide a score.
artificialfish
joined 1 week ago
All theoretical, but I would cut the decoder off a very smart chat model, then fine tune the encoder to provide a score on the rationality test dataset under CoT prompting.
Well I think you actually need to train a "discriminator" model on rationality tests. Probably an encoder only model like BERT just to assign a score to thoughts. Then you do monte carlo tree search.
Meta? The one that released Llama 3.3? The one that actually publishes its work? What are you talking about?
Why is it so hard to believe that deepseek is just yet another amazing paper in a long line of research done by everyone. Just because it’s Chinese? Everyone will adapt to this amazing innovation and then stagnate and throw compute at it until the next one. That’s how research works.
Not to mention China has billions of more people to establish a research community…
I think “just writing better code” is a lot harder than you think. You actually have to do research first you know? Our universities and companies do research too. But I guarantee using R1 techniques on more compute would follow the scaling law too. It’s not either or.
Nah, o1 has been out how long? They are already on o3 in the office.
It’s completely normal a year later for someone to copy their work and publish it.
It probably cost them less because they probably just distilled o1 XD. Or might have gotten insider knowledge (but honestly how hard could CoT fine tuning possibly be?)
Yes but also it’s open source soooo
https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-70B-Uncensored-i1-GGUF
They are fine at programming numpy and sympy given an interface, and they are surprisingly good at explaining advanced symbolic math concepts. I wouldn't expect them to be good at arithmetic, but a good reasoning model should be really good at mathematical reasoning.
Good catch, that’s probably what’s happening then
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