Recursive Reward Models - Are We Going to Need That Soon?

Hear about why OpenAI cites her work in RLHF and dialog models, approaches to rewards in RLHF, ChatGPT, Industry vs Academia, PsiPhi-Learning, AGI and more! 

Dr Natasha Jaques is a Senior Research Scientist at Google Brain.

Featured References

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard 

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck 

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar 

Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience
Marwa Abdulhai, Natasha Jaques, Sergey Levine 

Additional References  

• Fine-Tuning Language Models from Human Preferences, Daniel M. Ziegler et al 2019  
• Learning to summarize from human feedback, Nisan Stiennon et al 2020  
• Training language models to follow instructions with human feedback, Long Ouyang et al 2022