·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Building the enterprise environment for agentic AI1h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics3h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models8h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders8h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA8h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging8h◆Multi-Mask Diffusion Language Models for Few-Step Generation8h◆Solar Open 2 Technical Report8h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions8h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning8h◆H$^2$SD: Hybrid Hindsight Self-Distillation8h◆LunarFM: A Shared Multimodal Representation of the Moon's Surface8h◆Prior laundering: learned priors with inherited, undetectable overconfidence8h◆Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery8h◆Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting8h◆CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference8h◆Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support8h◆Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models8h◆A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data8h◆Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics8h◆Building the enterprise environment for agentic AI1h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics3h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models8h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders8h◆Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA8h◆Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging8h◆Multi-Mask Diffusion Language Models for Few-Step Generation8h◆Solar Open 2 Technical Report8h◆The Geometry of Personality: Activation Steering with Jungian Cognitive Functions8h◆Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning8h◆H$^2$SD: Hybrid Hindsight Self-Distillation8h◆LunarFM: A Shared Multimodal Representation of the Moon's Surface8h◆Prior laundering: learned priors with inherited, undetectable overconfidence8h◆Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery8h◆Prompt as a Data Type: In-Database LLM Prompt Management and Rewriting8h◆CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference8h◆Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support8h◆Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models8h◆A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data8h◆Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics8h◆
News/Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
arxiv
PublishedApril 30, 2026 at 4:00 AM
▲bullish

Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning

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

arXiv:2508.19900v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables learning effective policies from fixed datasets without any environment interaction. Existing methods typically employ policy constraints to mitigate the distribution shift encountered during offline RL t

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
04
#offline-rl#reinforcement-learning#machine-learning#optimization

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#offline-rl#reinforcement-learning#machine-learning#optimization

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models8harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders8harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA8harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging8h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews