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Bai et al.: iterated helpful-and-harmless preference training

Applied preference modeling and RLHF to helpful/harmless assistants and iterated online human feedback with regularly refreshed preference and policy models, studying capability compatibility and reward–KL behavior.

What is recorded here?

Applied preference modeling and RLHF to helpful/harmless assistants and iterated online human feedback with regularly refreshed preference and policy models, studying capability compatibility and reward–KL behavior.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

RLHF and preference learning predate this study. Helpfulness/harmlessness depend on annotator criteria and prompts, not certified safety. Empirical reward/KL relationships are scoped to tested settings; ConstitutionalAI/RLAIF is a separate later contribution.

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People and systems

Yuntao Bai; Andy Jones; Kamal Ndousse; Amanda Askell; Anna Chen; Nova DasSarma; Dawn Drain; Stanislav Fort; Deep Ganguli; Tom Henighan; Nicholas Joseph; Saurav Kadavath; Jackson Kernion; Tom Conerly; Sheer El-Showk; Nelson Elhage; Zac Hatfield-Dodds; Danny Hernandez; Tristan Hume; Scott Johnston; Shauna Kravec; Liane Lovitt; Neel Nanda; Catherine Olsson; Dario Amodei; Tom Brown; Jack Clark; Sam McCandlish; Chris Olah; Ben Mann; Jared Kaplan

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Research locations and affiliations

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English catalogue narrative

Bai et al.: iterated helpful-and-harmless preference training

Applied preference modeling and RLHF to helpful/harmless assistants and iterated online human feedback with regularly refreshed preference and policy models, studying capability compatibility and reward–KL behavior.

Scientific scope

RLHF and preference learning predate this study. Helpfulness/harmlessness depend on annotator criteria and prompts, not certified safety. Empirical reward/KL relationships are scoped to tested settings; ConstitutionalAI/RLAIF is a separate later contribution.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Yuntao Bai; Andy Jones; Kamal Ndousse; Amanda Askell; Anna Chen; Nova DasSarma; Dawn Drain; Stanislav Fort; Deep Ganguli; Tom Henighan; Nicholas Joseph; Saurav Kadavath; Jackson Kernion; Tom Conerly; Sheer El-Showk; Nelson Elhage; Zac Hatfield-Dodds; Danny Hernandez; Tristan Hume; Scott Johnston; Shauna Kravec; Liane Lovitt; Neel Nanda; Catherine Olsson; Dario Amodei; Tom Brown; Jack Clark; Sam McCandlish; Chris Olah; Ben Mann; Jared Kaplan

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources