Fulford: RFT is only worthwhile for out-of-distribution or make-or-break tasks
Isa Fulford · No Priors Ep. 112 | With OpenAI Deep Research, Isa Fulford · Apr 24, 2025 · at 7:50
OpenAI's Isa Fulford advises Sarah Guo on when startups should pursue reinforcement fine-tuning versus waiting for foundation model improvements.
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“I think if you have a very specific task that you think is so different to anything that the model was likely trained on and you try it a bunch of times yourself and you've tried a lot of different prompts and it's just really not good at it. So maybe it's genetic sequencing task or something that's just so out of distribution for the model that it doesn't know how to figure it out. I think that is a good time to try reinforcement fine tuning or If you have a task that is so critical to your, like, business workflow that getting the extra 10, 15% performance is really make or break, then probably try it. But if it's something that you think, oh, the model's pretty good at, but it gets things wrong, you know, some percentage of the time, and then you see with every next model that's released, it gets a little bit better, it might not be worth the effort if the model naturally is just going to get better at those things.”
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