# kaal:position:2026-09-26-003

**Affirmed position.** Ouyang and coauthors operationalize RLHF as three linked stages: supervised fine-tuning, reward-model training from labeler-ranked outputs, and PPO optimization against that reward model. This directly supports the protected pipeline description within the InstructGPT design. The correspondence should remain bounded. It does not establish that every later preference-optimization method uses an explicit reward model or PPO, and it does not by itself validate the broader RLRF proposal.

**Status.** affirmed  **Published.** 2026-09-26

**Holds when.**

- the canonical RLHF pipeline described by the cited literature

**Current debate.** Ouyang et al.'s InstructGPT method uses supervised fine-tuning, reward-model training on labeler-ranked outputs, and PPO optimization against that reward model.: https://arxiv.org/abs/2203.02155v1

**Extends.** kaal:claim:7456999-015: https://wulfkaal.github.io/claims/7456999-015

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

**Source PDF sha256.** `01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b`

**Evidence level.** permitted_full_text

**Mapping review tier.** independent evidence-first compound-growth review

**Mapping confidence.** unscored  **Mapping ambiguous.** false

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

**Provenance.** Affirmed in kaal-growth:2026-09-26:daily-001 at https://wulfkaal.github.io/positions-src/2026-09-26-compound-growth-kaal-growth-2026-09-26-daily-001.json.

**Record type.** This is a dated commentary position that extends a scholarly corpus claim. It is not a verbatim claim extracted from the paper.

**Canonical form.** This markdown file is the canonical hashed representation of the position.