·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI promises productivity, but for managers, it’s mostly a headache for now37m◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability1h◆OpenAI models are coming to Google Cloud2h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories3h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making10h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value10h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models10h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations10h◆When Synthetic Speech Is All You Have: Better Call GRPO10h◆Reducing belief in conspiracy theories as they unfold using large language models10h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors10h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization10h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning10h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding10h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment10h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions10h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents10h◆AI promises productivity, but for managers, it’s mostly a headache for now37m◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability1h◆OpenAI models are coming to Google Cloud2h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories3h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making10h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value10h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models10h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations10h◆When Synthetic Speech Is All You Have: Better Call GRPO10h◆Reducing belief in conspiracy theories as they unfold using large language models10h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors10h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization10h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning10h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding10h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment10h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions10h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents10h◆
News/RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
arxiv
PublishedJuly 18, 2026 at 4:00 AM
▲bullish

RAD: Retrieval High-quality Demonstrations to Enhance Decision-making

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

arXiv:2507.15356v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions. However, its reliance on finite static datasets inherently restricts the ability to generalize beyond the training

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
Mentioned models
01
  • 01
    Retrieval High-quAlity Demonstrations (RAD)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#offline-rl#generative-models

No replies yet. Be first.

Mentioned models
01
  • 01
    Retrieval High-quAlity Demonstrations (RAD)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#reinforcement-learning#offline-rl#generative-models

Related coverage

More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making10harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h
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