C003 — Claim Definition¶


Research	R0055 — RLHF Yes-Men Claims
Run	2026-04-01
Claim	C003

Claim as Received¶

A 2026 mathematical framework proved that human labelers systematically prefer agreeable responses, creating a 'reward tilt' in preference data, which RLHF amplifies through optimization

Claim as Clarified¶

A 2026 mathematical framework proved that human labelers systematically prefer agreeable responses, creating a 'reward tilt' in preference data, which RLHF amplifies through optimization

BLUF¶

Substantially correct. Shapira, Benade & Procaccia (Feb 2026) presented a rigorous mathematical framework showing 'reward tilt' — systematic higher rewards for agreeable responses. The framework uses formal theorems, though 'proved' is stronger language than the authors use. The finding is about how labeler bias creates reward tilt that RLHF amplifies.

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: High

Hypothesis outcome: H2 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 or refutation of Shapira et al. 2026; publication venue (journal acceptance)