arXiv (All)
open access
Reason-guided Human Reward Modeling for Preference-based Reinforcement Learning
Reinforcement learning (RL) policies optimized for task rewards may produce behaviors that are efficient but misaligned with human expectations, safety preferences, or intent. This paper presents a reason-guided preference-based reinforcement learning (PbRL) framework that learns a human reward mode
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
- Reason-guided Human Reward Modeling for Preference-based Reinforcement Learning#1375076
- RPS: Information Elicitation with Reinforcement Prompt Selection#14045
- Official Review · ICLR 2026#787023
- Specifying Reward Functions for RL Without Environment Sampling#1109541
- Learning Process Rewards via Reasoning State Propagation#1144187
Record · ID 1387817
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