arXiv (All)
open access
Rubric-Aware On-Policy Self-Distillation for LLM Personalization
LLM personalization aims to generate responses aligned with individual users' preferences and needs. User-specific rubrics make these expectations explicit, providing direct supervision on what a satisfactory answer should cover. Existing rubric-guided approaches, however, exploit such guidance only
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
- AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side#1108873
- AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side#1105724
- Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself#1071425
- HelpBench: Assessing the Ability of LLMs to Provide Privacy, Safety, and Security Advice#303142
- LLM-Guided Prompt Evolution for Password Guessing#13015
Record · ID 1129856
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