Dr. Nathan Lambert, an AI researcher, discusses offline reinforcement learning methods for RLHF and why industry labs may adopt them to avoid generation overhead during training.
“There's a few papers that people have published
Not a lot of traction.
I think it could take off.
Some people that I know in the RLHF area really think a lot of people are doing this in industry just because it makes the kind of training process simpler and the number of things you have to have running.”
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More from Nathan Lambert
AssertionSupported
Lambert: Tulu 3 matches or beats Meta Llama 3.1 on core evals
“On, like, core evals for our Suite of models from, I think, eight, seven D and four or five B is based on llama at the time. It's like it matches or beats meta on these core valves.”
Lambert: Deep Research relies on modular RL tasks rather than end-to-end outcomes
“I think the deep research blog post kind of hints that they do a bunch of small scale RL and then poof, the system works. Which I think is much more of what's happening is people train on a bunch of small things and they do some prompting and they see that whe…”
Lambert: Hybrid reasoners may be phased out except for niche uses
“I think in plenty of ways, like hybrid reasoners might just be aged out except for niche applications because quality is so much more important than having a hundred X less inference tokens. It's like you just pay for it and compute and that'll get better.”
Lambert: SFT cannot teach emergent tool use; models must learn via RL environments
“It's very easy to get the model to do tools if you prompt it to, but it's very hard to get the like RL model to learn that the tool is useful. And that's why it's to go through these things where it's like 80 failed tool uses and it still gets it or like it st…”
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