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In an era dominated by direct preference optimization and LLMasajudge, why do we still need a model to output only a scalar reward?
This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen.
Source code: https://github.com/natolambert/interconnects-tools
Original post: In an era dominated by direct preference optimization and LLM-as-a-judge, why do we still need a model to output only a scalar reward?
Podcast figures:
Figure 1: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reward-models/img_004.png
Figure 2: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reward-models/img_009.png
0:00 Why reward models are still key to understanding alignment
By Nathan Lambert4.1
99 ratings
In an era dominated by direct preference optimization and LLMasajudge, why do we still need a model to output only a scalar reward?
This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen.
Source code: https://github.com/natolambert/interconnects-tools
Original post: In an era dominated by direct preference optimization and LLM-as-a-judge, why do we still need a model to output only a scalar reward?
Podcast figures:
Figure 1: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reward-models/img_004.png
Figure 2: https://huggingface.co/datasets/natolambert/interconnects-figures/resolve/main/reward-models/img_009.png
0:00 Why reward models are still key to understanding alignment

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