·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend1h◆From Toast to Bessemer: Who founders will hear from at TechCrunch Founder Summit 20262h◆The iPhone Duo should have been an iPad4h◆Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)4h◆Now Google says it might use your Gmail and Google Photos data to target ads4h◆LitPivot: Developing research ideas through dynamic contextualization within the literature landscape4h◆Meta delays new business terms after publisher pushback on AI scraping6h◆Retro gaming is being overrun with AI9h◆Securing our frontier model training environment11h◆Retrieval Augmented Generation Compositionality17h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents17h◆TNODEV: Toolbox for Neural ODE Verification17h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding17h◆On the Expressive Power and Limitations of Multi-Layer SSMs17h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale17h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression17h◆A Generalisation Signal Need Not Be a Model-Selection Signal17h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering17h◆EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video17h◆Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility17h◆Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend1h◆From Toast to Bessemer: Who founders will hear from at TechCrunch Founder Summit 20262h◆The iPhone Duo should have been an iPad4h◆Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)4h◆Now Google says it might use your Gmail and Google Photos data to target ads4h◆LitPivot: Developing research ideas through dynamic contextualization within the literature landscape4h◆Meta delays new business terms after publisher pushback on AI scraping6h◆Retro gaming is being overrun with AI9h◆Securing our frontier model training environment11h◆Retrieval Augmented Generation Compositionality17h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents17h◆TNODEV: Toolbox for Neural ODE Verification17h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding17h◆On the Expressive Power and Limitations of Multi-Layer SSMs17h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale17h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression17h◆A Generalisation Signal Need Not Be a Model-Selection Signal17h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering17h◆EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video17h◆Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility17h◆
News/Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
arxiv
PublishedApril 18, 2026 at 4:00 AM
—neutral

Continuous-time reinforcement learning: ellipticity enables model-free value function approximation

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2602.06930v2 Announce Type: replace Abstract: We study off-policy reinforcement learning for controlling continuous-time Markov diffusion processes with discrete-time observations and actions. We consider model-free algorithms with function approximation that learn value and advantage function

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#markov-diffusions#function-approximation

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#markov-diffusions#function-approximation

Related coverage

More from ARXIV
arxivRetrieval Augmented Generation Compositionality17harxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents17harxivTNODEV: Toolbox for Neural ODE Verification17harxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding17h
The Bubble Brief
MON · WED · FRI

Read reinforcement-learning insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews