arXiv (All)
open access
Process Rewards for Outcome-Guided Reasoning Steps in LLM Reinforcement Learning
Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers can be checked automatically and converted into reliable training signals. Most such pipelines optimize outcome correctness only, which yields sparse f
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
- RPS: Information Elicitation with Reinforcement Prompt Selection#14045
- ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning#793014
- ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning#789010
- To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models#984116
- Hint-Guided Diversified Policy Optimization for LLM Reasoning#1187430
Record · ID 1342440
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