Best AI papers explained

145 Episodes

1. Driving Forces in AI: Scaling to 2025 and Beyond (Jason Wei, OpenAI)

   Published: 3/29/2025
2. Expert Demonstrations for Sequential Decision Making under Heterogeneity

   Published: 3/28/2025
3. TextGrad: Backpropagating Language Model Feedback for Generative AI Optimization

   Published: 3/27/2025
4. MemReasoner: Generalizing Language Models on Reasoning-in-a-Haystack Tasks

   Published: 3/27/2025
5. RAFT: In-Domain Retrieval-Augmented Fine-Tuning for Language Models

   Published: 3/27/2025
6. Inductive Biases for Exchangeable Sequence Modeling

   Published: 3/26/2025
7. InverseRLignment: LLM Alignment via Inverse Reinforcement Learning

   Published: 3/26/2025
8. Prompt-OIRL: Offline Inverse RL for Query-Dependent Prompting

   Published: 3/26/2025
9. Alignment from Demonstrations for Large Language Models

   Published: 3/25/2025
10. Q♯: Distributional RL for Optimal LLM Post-Training

    Published: 3/18/2025
11. Scaling Test-Time Compute Without Verification or RL is Suboptimal

    Published: 3/14/2025
12. Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

    Published: 3/14/2025
13. Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

    Published: 3/14/2025
14. Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

    Published: 3/14/2025
15. Revisiting Superficial Alignment Hypothesis

    Published: 3/14/2025
16. Diagnostic uncertainty: teaching language Models to describe open-ended uncertainty

    Published: 3/14/2025
17. Language Model Personalization via Reward Factorization

    Published: 3/14/2025
18. Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration

    Published: 3/14/2025
19. How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

    Published: 3/14/2025
20. Can Large Language Models Extract Customer Needs as well as Professional Analysts?

    Published: 3/13/2025