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Ai2’s open source Tülu 3 lets anyone play the AI post-training game
Ask anyone in the open source AI community, and they will tell you the gap between them and the big private companies is more than just computing power.
But the simple truth is that few developers have the chops to run their own LLMs to begin with, and even fewer can do post-training the way Meta, OpenAI, or Anthropic does — partly because they don’t know, but also because it’s technically complex and time-consuming. It’s a huge improvement over an earlier, more rudimentary post-training process (called, you guessed it, Tulu 2); in the nonprofit’s tests, this resulted in scores on par with the most advanced “open” models out there. Image Credits: AI2Basically, Tulu 3 covers everything from choosing which topics you want your model to care about — for instance, downplaying multilingual capabilities but dialing up math and coding — then takes it through a long regimen of data curation, reinforcement learning, fine tuning and preference tuning, plus tweaking a bunch of other meta-parameters and training processes that I couldn’t adequately describe to you.
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