Best AI papers explained

145 Episodes

1. AI in the Enterprise: Seven Lessons from Frontier Companies by OpenAI

   Published: 4/22/2025
2. Discussion: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

   Published: 4/21/2025
3. AI Agent Protocols and Human Preference

   Published: 4/21/2025
4. Cross-Environment Cooperation for Zero-Shot Multi-Agent Coordination

   Published: 4/20/2025
5. Sutton and Silver: The Era of Experience: Learning Beyond Human Data

   Published: 4/19/2025
6. Sample, Don't Search: Rethinking Test-Time Alignment for Language Models

   Published: 4/19/2025
7. AI Agents: Echoes of Past Technology Pivots?

   Published: 4/19/2025
8. Minimalist LLM Reasoning: Rejection Sampling to Reinforcement

   Published: 4/19/2025
9. Securing the Model Context Protocol in Enterprise Environments

   Published: 4/19/2025
10. Improving Multi-Turn Tool Use with Reinforcement Learning

    Published: 4/19/2025
11. Cultural Knowledge Conservation and Control in Large Language Models

    Published: 4/19/2025
12. Data Quality, Repetition, and Scaling of Language Models

    Published: 4/18/2025
13. Compute-Optimal Scaling Laws for Language Models Revisited

    Published: 4/18/2025
14. Concise Reasoning via Reinforcement Learning

    Published: 4/18/2025
15. Throughput Limits for LLM Inference and AI Agent Scheduling

    Published: 4/14/2025
16. RL Post-training Amplifies Pretraining Behaviors in Language Models

    Published: 4/14/2025
17. Fast Adaptation of Behavioral Foundation Models

    Published: 4/14/2025
18. Proprietary Reward Models: Sustaining Advantage in Agentic AI

    Published: 4/13/2025
19. Why Multi-Agent LLM Systems Fail: A Comprehensive Study

    Published: 4/12/2025
20. Play2Prompt: Zero-Shot Tool Instruction Optimization via Tool Play

    Published: 4/12/2025