Everything You Wanted to Know About LLM Post-Training, with Nathan Lambert of Allen Institute for AI
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What is the focus of this episode and who is the guest?
It's probably not worth the effort to spend all your time on preference tuning when you could just be making better data and better pipelines, which is what two through three is about, inspired by the transition we're seeing with the Llama report, with Trap Bot Arena, is like turned into a hockey stick again, where we have those incremental scores and the OpenAI and Google are like skyrocketing their scores. And the philosophy is like, How do we try to understand what the open groups should be doing and where there are hills to climb when you're increasing the complexity substantially of post-training? If you can have humans and LLMs do preference data, which do you send to humans versus LLMs? And that I think solves a lot of the problems, which is like there are definitely things that we want humans giving the answer on, but there are a lot of mechanical tasks that we can outsource LLMs.
There's a lot more at post-training. that is not really touched. And I think that the opportunity is high. Because the big thing is how do you develop character? Character is something that you don't have evaluation for in your models. And our models, if you compare them to Claude, will not have as consistent of a character.
Hello and welcome back to the Cognitive Revolution. Today my guest is Nathan Lambert, author of the popular Interconnects newsletter and machine learning researcher at the Allen Institute for AI, which today is releasing Tulu three, one of the most comprehensive open source efforts to diffuse the understanding and practice of frontier post training techniques for large language models that we have seen to date. By systematically working to match Meta's post training performance using the same Lama base model and sharing all of their findings and data publicly, Nathan and the team at the Allen Institute have illuminated what has historically been one of the most opaque aspects of large language model development.
And this conversation represents one of the most detailed discussions of this topic that you can find anywhere online today. We cover the full spectrum of post training techniques, including supervised fine tuning, multiple flavors of preference based reinforcement learning, and a new technique called reinforcement learning from verifiable reward, which rewards the model for accurately answering questions with objectively correct ground truth answers. At each step we dig into the practical details that make these techniques work, the associated compute requirements, data generation strategies, and the value derived from each, as well as the experimental designs that are used to measure performance while exploring the vast space of possible training recipes.
We even explore some fascinating emergent behaviors that echo the frontier reasoning capabilities that we've recently seen from OpenAIs 01. Nathan's frank discussion of both the technical and organizational challenges of this work, including in a few moments where he acknowledges aspects that are not yet well understood, provides a super useful window into what it takes to develop state of the art models. And the fact that they ultimately succeeded in matching Lama performance with a team of just 10 to 15 people makes their approach one to study closely. Now, how long this level of open development can continue into future generations of models remains, in my mind, an open question. Even with billionaire estate backing, human generated preference data and annotations are cost prohibitive for the Allen Institute, and the synthetic data generation techniques used in this project may or may not be available going forward if frontier developers follow OpenAI's lead and choose not to release their O one style reasoning traces.
That said, Nathan expects that the community will ultimately figure something out. And just before publishing, we've seen Chinese AGI company DeepSeek announce a new O1 style model called Deep Think, which does seem to show its work and is reportedly going to be open sourced in the near future.
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