·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Thinking Machines wants to build an AI that actually listens while it talks2h◆SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents3h◆Belief or Circuitry? Causal Evidence for In-Context Graph Learning3h◆Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare3h◆Muown: Row-Norm Control for Muon Optimization3h◆Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification3h◆Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models3h◆AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care3h◆Phoenix-VL 1.5 Medium Technical Report3h◆Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms3h◆OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control3h◆Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge3h◆Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care3h◆The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection3h◆Generalization Bounds of Emergent Communications for Agentic AI Networking3h◆DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules3h◆C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge3h◆MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction3h◆Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs3h◆Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations3h◆Thinking Machines wants to build an AI that actually listens while it talks2h◆SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents3h◆Belief or Circuitry? Causal Evidence for In-Context Graph Learning3h◆Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare3h◆Muown: Row-Norm Control for Muon Optimization3h◆Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification3h◆Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models3h◆AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care3h◆Phoenix-VL 1.5 Medium Technical Report3h◆Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms3h◆OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control3h◆Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge3h◆Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care3h◆The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection3h◆Generalization Bounds of Emergent Communications for Agentic AI Networking3h◆DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules3h◆C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge3h◆MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction3h◆Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs3h◆Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations3h◆
News/Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions
arxiv
PublishedMay 11, 2026 at 4:00 AM
▲bullish

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

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

arXiv:2512.20974v3 Announce Type: replace-cross Abstract: Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation by explicitly incorporating Bayesian task parameters into transition and reward models. However, cl

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#bayesian-inference#deep-learning

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#bayesian-inference#deep-learning

Related coverage

More from ARXIV
arxivSkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents3harxivBelief or Circuitry? Causal Evidence for In-Context Graph Learning3harxivMeasuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare3harxivMuown: Row-Norm Control for Muon Optimization3h
The Bubble Brief
WEEKLY

Read reinforcement-learning insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews