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Best AI papers explained

En podcast av: Enoch H. Kang

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

Our American listeners can also listen to Best AI papers explained at our American site

506 Avsnitt

Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences
Publicerades 2025-10-24

The Coverage Principle: How Pre-Training Enables Post-Training
Publicerades 2025-10-24
The Era of Real-World Human Interaction: RL from User Conversations
Publicerades 2025-10-24
Agent Learning via Early Experience
Publicerades 2025-10-24
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
Publicerades 2025-10-22
Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior
Publicerades 2025-10-22
A Definition of AGI
Publicerades 2025-10-22
Provably Learning from Language Feedback
Publicerades 2025-10-21
In-Context Learning for Pure Exploration
Publicerades 2025-10-21
On the Role of Preference Variance in Preference Optimization
Publicerades 2025-10-20
Training LLM Agents to Empower Humans
Publicerades 2025-10-20
Richard Sutton Declares LLMs a Dead End
Publicerades 2025-10-20
Demystifying Reinforcement Learning in Agentic Reasoning
Publicerades 2025-10-19
Emergent coordination in multi-agent language models
Publicerades 2025-10-19
Learning-to-measure: in-context active feature acquisition
Publicerades 2025-10-19
Andrej Karpathy's insights: AGI, Intelligence, and Evolution
Publicerades 2025-10-19
Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data
Publicerades 2025-10-18
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Publicerades 2025-10-18
The attacker moves second: stronger adaptive attacks bypass defenses against LLM jail- Breaks and prompt injections
Publicerades 2025-10-18
When can in-context learning generalize out of task distribution?
Publicerades 2025-10-16

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

506 Avsnitt

Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences

The Coverage Principle: How Pre-Training Enables Post-Training

The Era of Real-World Human Interaction: RL from User Conversations

Agent Learning via Early Experience

Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL

Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior

A Definition of AGI

Provably Learning from Language Feedback

In-Context Learning for Pure Exploration

On the Role of Preference Variance in Preference Optimization

Training LLM Agents to Empower Humans

Richard Sutton Declares LLMs a Dead End

Demystifying Reinforcement Learning in Agentic Reasoning

Emergent coordination in multi-agent language models

Learning-to-measure: in-context active feature acquisition

Andrej Karpathy's insights: AGI, Intelligence, and Evolution

Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data

Representation-Based Exploration for Language Models: From Test-Time to Post-Training

The attacker moves second: stronger adaptive attacks bypass defenses against LLM jail- Breaks and prompt injections

When can in-context learning generalize out of task distribution?