OpenAI's o1 and o3 aren't just better language models—they actually think. You'll learn how reinforcement learning creates genuine reasoning capabilities, but also discover the dark side: "mode collapse" creates an artificial hivemind where models converge to eerily similar responses. The uncomfortable truth? Even the best RL refines existing knowledge rather than discovering new concepts, and there's a 1000x gap in data efficiency between AI and human brains. This episode cuts through the hype around reasoning models to show you what's real and what's still missing.


Topics Covered
- Large Reasoning Models (LRMs) vs. traditional LLMs
- Reinforcement learning mechanics (explained accessibly)
- The mode collapse problem (AI converging to similar responses)
- Data scaling wall and synthetic data challenges
- Why small models (32B parameters) are rising in importance
- The verification crisis in AI deployment

The Reasoning Revolution

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This podcast series cuts through AI hype to deliver what technical professionals actually need: honest assessments of what works, what fails, and why it matters. Curated from NeurIPS 2025 — the world's premier AI research conference — these 11 episodes translate cutting-edge research into accessible narratives without dumbing down the substance.

Basisset.com

The Reasoning Revolution

NeurIPS 2025 by Basis Set

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