Summary of Asynchronous Rlhf: Faster and More Efficient Off-policy Rl For Language Models, by Michael Noukhovitch et al.
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
by Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron Courville
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach in this research paper separates generation and learning in Reinforcement Learning with Human Feedback (RLHF), allowing for asynchronous generation of new samples while simultaneously training on old samples. This enables faster training and more compute-optimal scaling, but also introduces the challenge of learning from off-policy data. The authors test several RLHF algorithms and find that online DPO is most robust to off-policy data, with robustness increasing with the scale of the policy model. They demonstrate the scalability of asynchronous RLHF by training a general-purpose chatbot 40% faster than a synchronous run while matching final performance. Finally, they extend their results to math and reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new approach to Reinforcement Learning with Human Feedback (RLHF). Right now, most RLHF is done online and on-policy, which means it takes a long time and uses a lot of computer power. The authors want to make RLHF faster and more efficient by separating the generation of new samples from learning from old ones. This lets them generate new samples while they’re still training on older ones, making it faster and using less computer power. But this also means they have to learn from data that’s not as good as what they would get if they were doing things online and on-policy. The authors test different ways of doing RLHF and find that one way is better than others at handling this kind of off-policy data. They show that their approach can be used to train a chatbot 40% faster than before while still getting good results. |
Keywords
» Artificial intelligence » Reinforcement learning » Rlhf
