arXiv (All)
open access
CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning
Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing rewards across rollouts of the same prompt. For multiple rewards, GRPO sums the reward components and normalizes the total reward by its within-group
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
- Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning#158708
- MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR#1129594
- Cliff: Learning Process Rewards from the First Mistake#924942
- Cliff: Learning Process Rewards from the First Mistake#920708
- MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary#1143029
Record · ID 1132021
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