arXiv (All)
open access
MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning
Rubric-based reinforcement learning extends reward-driven optimization to open-ended tasks by assigning partial credit to individual response requirements. However, rubric judges can assign a high criterion score even when the information or action it requires is absent from the response, a failure
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
- QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards#793710
- QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards#789706
- PreFER: Interactive Robo-Advisor with Scoring Mechanism#1188084
- To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization#467154
- PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning#798876
Record · ID 1187138
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