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Secrets of RLHF in Large Language Models Part I: PPO
This paper discusses the challenges and importance of aligning large language models (LLMs) with humans. It proposes an advanced version of the Proximal Policy Optimization (PPO) algorithm to improve training stability and shares open-source implementations to contribute to LLM advancement.
https://arxiv.org/abs//2307.04964
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper discusses the challenges and importance of aligning large language models (LLMs) with humans. It proposes an advanced version of the Proximal Policy Optimization (PPO) algorithm to improve training stability and shares open-source implementations to contribute to LLM advancement.
https://arxiv.org/abs//2307.04964
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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