C005 — Claim Definition¶


Research	R0055 — RLHF Yes-Men Claims
Run	2026-04-01
Claim	C005

Claim as Received¶

Curating anti-sycophancy preference pairs reduces sycophancy by 84-85%, without changing the RLHF algorithm

Claim as Clarified¶

Curating anti-sycophancy preference pairs reduces sycophancy by 84-85%, without changing the RLHF algorithm

BLUF¶

Correct with attribution caveat. Khan et al. (IEEE BigData 2024) achieved 85% reduction in persona-based tests and 84% in preference-driven tests using DPO with curated anti-sycophancy preference pairs. The method uses DPO rather than RLHF, so 'without changing the RLHF algorithm' is accurate in spirit — the intervention is in the data, not the optimization approach.

Scope¶

Domain: AI alignment, sycophancy, enterprise AI
Timeframe: 2022-2026
Testability: Verifiable against published research and documentation

Assessment Summary¶

Probability: Very likely (80-95%)

Confidence: Medium

Hypothesis outcome: H1 prevails — see assessment for details.

[Full assessment in assessment.md.]

Status¶

Field	Value
Date created	2026-04-01
Date completed	2026-04-01
Researcher profile	Phillip Moore
Prompt version	Unified Research Methodology v1
Revisit by	2026-10-01
Revisit trigger	Replication of the 84-85% figures on different models or larger datasets