RLHF vs TrainingData-Label-Again-based-on-Reward.
Reward come from human labeling.
"Reinforcement Learning from Human Feedback" and "Data Re-Label from Reward" are two different approaches for training a dialogue model using reinforcement learning (RL).
The main difference between these two approaches is the source of the feedback that is used to train the model. "Reinforcement Learning from Human Feedback" involves providing the model with real-time feedback from humans, while "Data Re-Label from Reward" involves re-labeling a dataset with rewards or penalties based on the model's responses.
Dear university staff!
I inform you that my lecture on electronic medicine on the topic: "The use of automated system-cognitive analysis for the classification of human organ tumors" can be downloaded from the site: https://www.patreon.com/user?u =87599532
Lecture with sound in English. You can download it and listen to it at your convenience.
Sincerely,
Vladimir Ryabtsev, Doctor of Technical Science, Professor Information Technologies.
Dear university staff!
I inform you that my lecture on electronic medicine on the topic: "The use of automated system-cognitive analysis for the classification of human organ tumors" can be downloaded from the site: https://www.patreon.com/user?u =87599532
Lecture with sound in English. You can download it and listen to it at your convenience.
Sincerely,
Vladimir Ryabtsev, Doctor of Technical Science, Professor Information Technologies.
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