Summary of Proxy-rlhf: Decoupling Generation and Alignment in Large Language Model with Proxy, by Yu Zhu et al.
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
by Yu Zhu, Chuxiong Sun, Wenfei Yang, Wenqiang Wei, Bo Tang, Tianzhu Zhang, Zhiyu Li, Shifeng Zhang, Feiyu Xiong, Jie Hu, Mingchuan yang
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Reinforcement Learning from Human Feedback (RLHF) is a crucial approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods are computationally expensive due to simultaneously assigning generation and alignment tasks to the LLM. This paper introduces Proxy-RLHF, a novel method that decouples these processes, achieving alignment at a significantly lower computational cost. The approach involves designing a Markov Decision Process (MDP) for the alignment process and training a proxy model using Reinforcement Learning (RL) to oversee token generation of the LLM without altering it. Experimental results demonstrate that Proxy-RLHF achieves comparable alignment with only 1% of the training parameters required by other methods. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making sure Large Language Models (LLMs) behave in a way that humans agree with. Right now, the way we do this is very resource-intensive because it tries to do two things at once. The new method proposed here, called Proxy-RLHF, does these tasks separately and makes it much more efficient. It uses a special process called Markov Decision Process (MDP) to make sure the LLM is aligned with human values. This approach trains a smaller model that helps guide the LLM’s language generation without changing the LLM itself. The results show that this new method works just as well as the old one, but takes much less computational power. |
Keywords
* Artificial intelligence * Alignment * Reinforcement learning * Reinforcement learning from human feedback * Rlhf * Token