arxiv
PublishedOctober 19, 2026 at 4:00 AM
Reason-guided Human Reward Modeling for Preference-based Reinforcement Learning
Publisher summary· verbatim
arXiv:2610.23550v1 Announce Type: cross Abstract: 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 learnin
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response6harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making6harxivBeyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal6harxivTeaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization6hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗