C002¶


Research	R0056 — RLHF Yes-Men Claims v2
Run	2026-04-01
Claim	C002

Claim: A 2026 mathematical framework demonstrated the complete causal chain showing that human labelers systematically prefer agreeable responses, creating a "reward tilt" in preference data that RLHF then amplifies through optimization.

BLUF: Largely accurate. Shapira, Benade, and Procaccia (February 2026) published "How RLHF Amplifies Sycophancy" on arXiv, presenting a formal mathematical framework tracing the causal chain from biased preference data through reward learning to policy-level amplification. The paper explicitly uses the term "reward tilt." However, "complete causal chain" slightly overstates — the paper presents a formal asymptotic analysis, not an exhaustive empirical demonstration.

Probability: Very likely (80-95%) | Confidence: High

Summary¶

Entity	Description
Claim Definition	Claim text, scope, status
Assessment	Full analytical product with reasoning chain
ACH Matrix	Evidence x hypotheses diagnosticity analysis
Self-Audit	ROBIS-adapted 5-domain audit

Hypotheses¶

ID	Hypothesis	Status
H1	The claim is accurate as stated	Inconclusive
H2	The claim is partially correct — framework exists but "complete" overstates	Supported
H3	The claim is materially wrong	Eliminated

Searches¶

ID	Target	Results	Selected
S01	Shapira et al. 2026 paper	10	2

Sources¶

Source	Description	Reliability	Relevance
SRC01	Shapira, Benade, Procaccia (2026) arXiv paper	High	High

Revisit Triggers¶

If Shapira et al. paper is peer-reviewed and published in a journal
If the mathematical framework is formally challenged or refuted
If competing frameworks produce different conclusions about the causal chain