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Best AI papers explained

Por: Enoch H. Kang

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

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Latest episodes of the podcast Best AI papers explained

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Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL 22/10/2025

Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior 22/10/2025

A Definition of AGI 22/10/2025

Provably Learning from Language Feedback 21/10/2025

In-Context Learning for Pure Exploration 21/10/2025

On the Role of Preference Variance in Preference Optimization 20/10/2025

Training LLM Agents to Empower Humans 20/10/2025

Richard Sutton Declares LLMs a Dead End 20/10/2025

Demystifying Reinforcement Learning in Agentic Reasoning 19/10/2025

Emergent coordination in multi-agent language models 19/10/2025

Learning-to-measure: in-context active feature acquisition 19/10/2025

Andrej Karpathy's insights: AGI, Intelligence, and Evolution 19/10/2025

Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data 18/10/2025

Representation-Based Exploration for Language Models: From Test-Time to Post-Training 18/10/2025

The attacker moves second: stronger adaptive attacks bypass defenses against LLM jail- Breaks and prompt injections 18/10/2025

When can in-context learning generalize out of task distribution? 16/10/2025

The Art of Scaling Reinforcement Learning Compute for LLMs 16/10/2025

A small number of samples can poison LLMs of any size 16/10/2025

Dual Goal Representations 14/10/2025

Welcome to the Era of Experience 14/10/2025

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