# kaal:claim:7456999-057

**Claim.** Remark 1 states only signal invariance and supports no policy-level consequence without a separability model and empirical training evidence.

**Type.** failure  **Support.** asserted

**Holds when.**

- the style-invariance remark

**Source quote.**

> This is a restatement of signal invariance. It adds nothing to reinforcement learning from verifiable rewards. It does not show that a pool resolution is style-independent. It does not show that a fitted reward model inherits the invariance of . It also says nothing about how a trained policy allocates gradient effort. Any policy-level claim would require a stated separability model and empirical training evidence. Neither is present here.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), IV.D Remark 1: A Style-Independent Signal Is Invariant to Style, page 20

**Cite as.** Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999

**Verify.** sha256 of source PDF `01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Reinforcement%20Learning%20from%20Reputation%20Feedback.pdf

**Topics.** ai-and-agents, research-methods, risk-and-incentives

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