Reinforcement Learning Insights
Google has confirmed the use of the vanilla policy gradient algorithm for reinforcement learning from human feedback, marking a shift in their approach. The incorporation of KL penalties in various stages of the reward function ensures that updates remain close to human annotations. Additionally, leveraging high-capacity models during the RLHF process highlights the importance of nuanced understanding in language models, paving the way for more effective and cost-efficient AI development.In this clip
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Google ships it: Gemma open LLMs and Gemini backlash
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