Human Preferences Help Scientists Train AI 30x Faster Than Before

Written by languagemodels | Published 2024/12/03
Tech Story Tags: reinforcement-learning | in-context-learning | preference-learning | reward-functions | ai-training | rlhf-efficiency | human-in-the-loop-rl | hackernoon-top-story

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Table of Links

  1. Abstract and Introduction
  2. Related Work
  3. Problem Definition
  4. Method
  5. Experiments
  6. Conclusion and References

A. Appendix

A.1. Full Prompts and A.2 ICPL Details

A. 3 Baseline Details

A.4 Environment Details

A.5 Proxy Human Preference

A.6 Human-in-the-Loop Preference

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Authors:

(1) Chao Yu, Tsinghua University;

(2) Hong Lu, Tsinghua University;

(3) Jiaxuan Gao, Tsinghua University;

(4) Qixin Tan, Tsinghua University;

(5) Xinting Yang, Tsinghua University;

(6) Yu Wang, with equal advising from Tsinghua University;

(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;

(8) Eugene Vinitsky, with equal advising from New York University ([email protected]).


This paper is available on arxiv under CC 4.0 license.


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