Best AI papers explained

En podcast av Enoch H. Kang

550 Avsnitt

Past-Token Prediction for Long-Context Robot Policies
Publicerades: 2025-05-20
Recovering Coherent Event Probabilities from LLM Embeddings
Publicerades: 2025-05-20
Systematic Meta-Abilities Alignment in Large Reasoning Models
Publicerades: 2025-05-20
Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers
Publicerades: 2025-05-20
Efficient Exploration for LLMs
Publicerades: 2025-05-19
Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation
Publicerades: 2025-05-18
Bayesian Concept Bottlenecks with LLM Priors
Publicerades: 2025-05-17
Transformers for In-Context Reinforcement Learning
Publicerades: 2025-05-17
Evaluating Large Language Models Across the Lifecycle
Publicerades: 2025-05-17
Active Ranking from Human Feedback with DopeWolfe
Publicerades: 2025-05-16
Optimal Designs for Preference Elicitation
Publicerades: 2025-05-16
Dual Active Learning for Reinforcement Learning from Human Feedback
Publicerades: 2025-05-16
Active Learning for Direct Preference Optimization
Publicerades: 2025-05-16
Active Preference Optimization for RLHF
Publicerades: 2025-05-16
Test-Time Alignment of Diffusion Models without reward over-optimization
Publicerades: 2025-05-16
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Publicerades: 2025-05-16
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
Publicerades: 2025-05-16
Advantage-Weighted Regression: Simple and Scalable Off-Policy RL
Publicerades: 2025-05-16
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Publicerades: 2025-05-16
Transformers can be used for in-context linear regression in the presence of endogeneity
Publicerades: 2025-05-15

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Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

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550 Avsnitt

Past-Token Prediction for Long-Context Robot Policies

Recovering Coherent Event Probabilities from LLM Embeddings

Systematic Meta-Abilities Alignment in Large Reasoning Models

Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers

Efficient Exploration for LLMs

Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation

Bayesian Concept Bottlenecks with LLM Priors

Transformers for In-Context Reinforcement Learning

Evaluating Large Language Models Across the Lifecycle

Active Ranking from Human Feedback with DopeWolfe

Optimal Designs for Preference Elicitation

Dual Active Learning for Reinforcement Learning from Human Feedback

Active Learning for Direct Preference Optimization

Active Preference Optimization for RLHF

Test-Time Alignment of Diffusion Models without reward over-optimization

Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment

Advantage-Weighted Regression: Simple and Scalable Off-Policy RL

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective

Transformers can be used for in-context linear regression in the presence of endogeneity