arXiv (All)
open access
Rubric Rewards from Item Response Theory
Many language tasks have no single answer that can be checked automatically. Rubrics provide criteria for judging responses to these tasks. For reinforcement learning, the resulting verdicts must be combined into a scalar reward. A common approach sums the points assigned to satisfied criteria. Dist
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
- From Soft Targets to Reward Signals: How Assignment and Reward Objectives Interact#1129463
- Beyond Math and Code: Lightweight Corpus-Grounded Process Rewards for Factual Question Answering#1116306
- Visual Preference Optimization with Rubric Rewards#13120
- Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning#996895
- Building Legal Reward Models for Grounding and Abstention#1084664
Record · ID 1130300
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