Unverified50% confidenceFactExact time
The sycophancy problem refers to models reinforcing accommodating behavior during RLHF training because responses that satisfy users receive higher human ratings, even at the cost of accuracy or safety.
1
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50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
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Related Claims
UnverifiedResearch from institutions including OpenAI and Anthropic indicates that models trained with RLHF systematically bias toward producing answers that satisfy raters rather than answers that are objectively accurate.71% similarUnverifiedCapability guardrails typically include value alignment during RLHF training, content filtering at the inference layer, and legal constraints from usage policies.70% similarUnverifiedThe higher priority of system prompts over user instructions is deliberately reinforced through dedicated alignment training data during the RLHF stage.70% similarUnverifiedSafety alignment refers to the ability to train a model to refuse to cooperate with harmful requests, with common techniques including RLHF and Constitutional AI.70% similarUnverifiedThe alignment field encompasses techniques such as reward modeling, reinforcement learning from human feedback (RLHF), release review, and red-teaming68% similar
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