Best AI papers explained

506 Épisodes

1. The Art of Scaling Reinforcement Learning Compute for LLMs

   Publié: 16/10/2025
2. A small number of samples can poison LLMs of any size

   Publié: 16/10/2025
3. Dual Goal Representations

   Publié: 14/10/2025
4. Welcome to the Era of Experience

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

   Publié: 14/10/2025
6. Self-Adapting Language Models

   Publié: 12/10/2025
7. The Markovian Thinker

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

   Publié: 12/10/2025
9. Transformer Predictor Dynamics and Task Diversity

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

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

    Publié: 11/10/2025
12. Understanding Prompt Tuning and In-Context Learning via Meta-Learning

    Publié: 11/10/2025
13. MLPs Learn In-Context on Regression and Classification tasks

    Publié: 11/10/2025
14. Is Pre-Training Truly Better than Meta-Learning?

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

    Publié: 11/10/2025
16. Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs

    Publié: 09/10/2025
17. Learning dynamics of LLM finetuning

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

    Publié: 09/10/2025
19. OpenAI Agent Builder and n8n: Orchestrating Reasoning Versus Automating Process

    Publié: 08/10/2025
20. Training Agents Inside of Scalable World Models

    Publié: 08/10/2025