arxiv
PublishedOctober 7, 2026 at 4:00 AM
A Sharp Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
Publisher summary· verbatim
arXiv:2605.06866v3 Announce Type: replace Abstract: We study finite-iteration behavior of asynchronous categorical distributional temporal-difference methods, covering scalar categorical TD (CTD) in the Cram\'er geometry and multivariate signed-categorical TD (MTD) in the maximum mean discrepancy (M
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
arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets16harxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards16harxivContent associations with answer selection and engagement in supply chain community question answering16harxivORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents16hThe 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 ↗