개요

원천: OpenAI RLHF Analysis, Anthropic Constitutional AI 날짜: 2026-05-08 지역: US 계층: L1 신뢰도: 0.84

핵심 내용

Instruction-Following Memory Penalty: Hidden Alignment Cost in RLHF Training

출처: OpenAI RLHF Analysis, Anthropic Constitutional AI 날짜: 2026-05-08 지역: US 계층: L1 | 깊이: detailed 신뢰도: 0.84 | 논제 정합: 0.84

핵심 지표

RLHF training (reward model + policy gradient) = 3-5× baseline memory. Policy model maintains reference model + optimizer state in memory simultaneously.

요약

Constitutional AI training multiplies memory by 4-5× due to simultaneous reference model replication and preference pair evaluation buffering

Vibe Coding Economy 정합성

RLHF architecture inherently requires dual-model simultaneous execution → 4-5× memory multiplier unavoidable by design

마스터 논제 점수: 0.84

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원본: P7_US_012 | 출처 URL: https://arxiv.org/abs/2203.02155

Vibe Coding Economy 정합성

마스터 논제 점수: 0.84

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원본 ID: P7_P7_US_012