Exploring Various Aspects of Reinforcement Learning from Human Feedback and Pluralistic Alignment in AI Systems

Integrating some non computing science into reinforcement learning from human feedback can give us the models we want.
This is AI generated audio with Python and 11Labs.
Source code: https://github.com/natolambert/interconnects-tools
Original post: https://www.interconnects.ai/p/reinventing-llm-alignment

0:00 Stop "reinventing" everything to "solve" AI alignment
2:19 Social Choice for AI Alignment: Dealing with Diverse Human Feedback
7:03 OLMo 1.7 7B: A truly open model with actually good benchmarks

Fig 1: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reinvention/img_013.png
Fig 2: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reinvention/img_015.png
Fig 3: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reinvention/img_018.png
Fig 4: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reinvention/img_024.png
Fig 5: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reinvention/img_027.png

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