Reviewers rank two draft notices; those rankings later teach a pref…

Question

Reviewers rank two draft notices; those rankings later teach a preference model that nudges the generator toward the preferred style and safety. Which alignment path is that?

Accepted Answer

Reinforcement learning from human feedback (RLHF): human preference ranks align the model after pretraining. No reward-model math is required. — Reviewers rank two draft notices. Those ranks later teach a preference model that nudges the generator toward the preferred style and safety. That path is RLHF. No reward-model math is required, and it is not a BERT blank, a tree split, or a tensor-parallel homework.

Answer

Masked language modeling only, because ranking two notices is filling a BERT blank.

Answer

XGBoost on sensor columns, because a preference rank is a harvest total.

Answer

NCCL tensor-parallel design, because a ranked notice is a Professional collective.

Reviewers rank two draft notices; those rankings later teach a preference model that nudges the generator toward the preferred style and safety. Which alignment path is that?

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