Why it matters

Reward model scaling shapes RLHF quality. Understanding shapes design.

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The architecture

Reward model size vs quality.

Preference data scale.

Over-optimization curves.

Reward model scalingRM sizecapacityPreference dataquality + quantityOver-optimizationdiminishing returnsLarger RM helps but data quality dominates; over-optim is real risk
Reward scaling.
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How it works end to end

Gao et al. 2023.

Bradley-Terry loss standard.

Over-optimization: KL to reference regularizes.