Agreement: Ouyang et al.'s InstructGPT method uses supervised fine-tuning, reward-model training on labeler-ranked outputs, and PPO optimization against that reward model.
Ouyang and coauthors operationalize RLHF as three linked stages: supervised fine-tuning, reward-model training from labeler-ranked outputs, and PPO optimization against that reward model. This directly supports the protected pipeline description within the InstructGPT design. The correspondence should remain bounded. It does not establish that every later preference-optimization method uses an explicit reward model or PPO, and it does not by itself validate the broader RLRF proposal.
Affirmed commentary position. This record extends a source-bound scholarly claim but is not a verbatim paper claim.
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