·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Sony Music, Warner sue Anthropic, alleging a “brazen campaign” of intellectual property theft6h◆Sony Music and Warner Chappell are suing Anthropic6h◆“We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z7h◆Nvidia’s AI advantage is moving beyond the GPU11h◆Musicians-turned-detectives are hunting for AI grifters12h◆Fine-Tuning of Transformer models with Frames20h◆Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection20h◆Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search20h◆Syntax vs. Semantics: How Transformers Learn Deep Dependencies20h◆NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation20h◆FaulT-Bench: Towards Benchmarking Network Troubleshooting LLM Agents under Unreliable User Tickets20h◆Unsupervised Post-Training of Foundation Models: A Survey20h◆Drift-Adaptive ICU Intervention Prediction: Freezing the Physiological Encoder for Auditable Model Updating20h◆Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement20h◆Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments20h◆Magnon-induced phononic Chern insulator20h◆Five Primitives for Governing Autonomous AI Agents at Runtime20h◆AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design20h◆Categorizer Automata for Discounted-Sum Payoffs20h◆FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes20h◆Sony Music, Warner sue Anthropic, alleging a “brazen campaign” of intellectual property theft6h◆Sony Music and Warner Chappell are suing Anthropic6h◆“We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z7h◆Nvidia’s AI advantage is moving beyond the GPU11h◆Musicians-turned-detectives are hunting for AI grifters12h◆Fine-Tuning of Transformer models with Frames20h◆Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection20h◆Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search20h◆Syntax vs. Semantics: How Transformers Learn Deep Dependencies20h◆NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation20h◆FaulT-Bench: Towards Benchmarking Network Troubleshooting LLM Agents under Unreliable User Tickets20h◆Unsupervised Post-Training of Foundation Models: A Survey20h◆Drift-Adaptive ICU Intervention Prediction: Freezing the Physiological Encoder for Auditable Model Updating20h◆Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement20h◆Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments20h◆Magnon-induced phononic Chern insulator20h◆Five Primitives for Governing Autonomous AI Agents at Runtime20h◆AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design20h◆Categorizer Automata for Discounted-Sum Payoffs20h◆FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes20h◆
News/Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
arxiv
PublishedMay 28, 2026 at 4:00 AM

Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach

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

arXiv:2605.27834v1 Announce Type: new Abstract: We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environment. This arises naturally when demonstrations are collected in a controll

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivFine-Tuning of Transformer models with Frames20harxivFeature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection20harxivNaive Prompt Optimization: Rethinking the Need for Complex Prompt Search20harxivSyntax vs. Semantics: How Transformers Learn Deep Dependencies20h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews