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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.

620 episodios disponibles

Latest episodes of the podcast Best AI papers explained

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Value Flows: Flow-Based Distributional Reinforcement Learning 14/10/2025

Self-Adapting Language Models 12/10/2025

The Markovian Thinker 12/10/2025

Moloch’s Bargain: emergent misalignment when LLMs compete for audiences 12/10/2025

Transformer Predictor Dynamics and Task Diversity 11/10/2025

Base models know how to reason, thinking models learn when 11/10/2025

Spectrum tuning: Post-training for distributional coverage and in-context steerability 11/10/2025

Understanding Prompt Tuning and In-Context Learning via Meta-Learning 11/10/2025

MLPs Learn In-Context on Regression and Classification tasks 11/10/2025

Is Pre-Training Truly Better than Meta-Learning? 11/10/2025

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models 11/10/2025

Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs 09/10/2025

Learning dynamics of LLM finetuning 09/10/2025

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF 09/10/2025

OpenAI Agent Builder and n8n: Orchestrating Reasoning Versus Automating Process 08/10/2025

Training Agents Inside of Scalable World Models 08/10/2025

Small Language Models are the Future of Agentic AI 07/10/2025

Activation Steering in Generative Settings via Contrastive Causal Mediation Analysis 06/10/2025

Eliciting Secret Knowledge from Language Models 06/10/2025

Temporal difference flow 06/10/2025

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