arXiv (All)
open access
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning
Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-ended tasks. However, standard methods compress criterion-level feedback into scalar rewards and assign uniform token advantages,
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
- Small Language Models as Judges for Rubric-Based Reinforcement Learning#799249
- Small Language Models as Judges for Rubric-Based Reinforcement Learning#797826
- SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning#1155773
- QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards#793710
- QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards#789706
Record · ID 1342942
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