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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response6h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal6h◆Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization6h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents6h◆AIMO Interpretability Challenge6h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD6h◆ECHO: An Auditable Dual-Path Memory System for Long-Horizon Agents6h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value6h◆Differential Functionalism: Jacobians Shape the Structure of Phenomenal Experience6h◆Unified Agentic Video Editing Across Levels of Complexity and Creativity6h◆ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models6h◆Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning6h◆Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations6h◆Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check6h◆TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU6h◆AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment6h◆HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference6h◆Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models6h◆Entropic perspective on AI resilience, data-minimal learning and optimal control6h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response6h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal6h◆Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization6h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents6h◆AIMO Interpretability Challenge6h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD6h◆ECHO: An Auditable Dual-Path Memory System for Long-Horizon Agents6h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value6h◆Differential Functionalism: Jacobians Shape the Structure of Phenomenal Experience6h◆Unified Agentic Video Editing Across Levels of Complexity and Creativity6h◆ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models6h◆Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning6h◆Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations6h◆Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check6h◆TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU6h◆AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment6h◆HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference6h◆Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models6h◆Entropic perspective on AI resilience, data-minimal learning and optimal control6h◆
News/Learning to summarize with human feedback
openai
PublishedSeptember 4, 2020 at 7:00 AM

Learning to summarize with human feedback

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We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.

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