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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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Personalized reasoning: just-in-time personalization and why LLMs fail at it 05/10/2025

Prompt Curriculum Learning for Efficient LLM Post-Training 05/10/2025

Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning 04/10/2025

Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward 04/10/2025

Learning to summarize user information for personalized reinforcement learning from human feedback 04/10/2025

Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF 03/10/2025

LIMI: Less is More for Agency 01/10/2025

LoRA Without Regret 01/10/2025

Actor-Critic without Actor: Critic-Guided Denoising for RL 29/09/2025

DELTA-Code: How Does RL Unlock and Transfer New Programming Algorithms in LLMs? 29/09/2025

Linear Transformers Implicitly Discover Unified Numerical Algorithms 29/09/2025

Regularizing Extrapolation in Causal Inference 27/09/2025

DoubleGen - Debiased Generative Modeling of Counterfactuals 27/09/2025

What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT 27/09/2025

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision 27/09/2025

Learning without training: The implicit dynamics of in-context learning 24/09/2025

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model 24/09/2025

Open Problems in Mechanistic Interpretability 21/09/2025

Maestro: Joint Graph & Config Optimization for Reliable AI Agents 21/09/2025

Thought Anchors: Which LLM Reasoning Steps Matter? 21/09/2025

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