arxiv
PublishedOctober 12, 2026 at 4:00 AM
Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parametric Policies
Publisher summary· verbatim
arXiv:2602.23811v5 Announce Type: replace-cross Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) have established the theoretical foundations of learning a good policy from offline
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
arxivSmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices15harxivVETR: Execution-Grounded Self-Improving Agent for Symbolic Graphics Reasoning15harxivAI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware15harxivSEEDS: Preview-Guided Evidence Admission for Search Agents15hThe 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 ↗