arXiv (All)
open access
Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback
Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ultimately selecting a single collective policy. We study this problem in offline reinforcement learning from human feedback (RLHF), where the party associated wit
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary#1143029
- RMB: Reward Model Boosting Mitigates Reward Hacking#1117933
- Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews#609818
- Procedural Objective AI (POAI): A Multi-Inference Framework for Deliberation#281776
- Anchored Sequential Deliberation#1122686
Record · ID 974145
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive