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How do we make AI reason like humans—and maybe better? Microsoft Research is tackling this with breakthrough techniques to enhance how language models think and solve problems. 

In this episode of the Colaberry AI Podcast, we dive deep into the latest frameworks and methods that could reshape the future of AI reasoning—from small models to massive LLMs.

What we cover:
 🧠 Three key strategies to elevate LLM reasoning across domains
🧮 rStar-Math & Logic-RL: New frameworks for making smaller models smarter
📐 LIPS & Neuro-Symbolic Systems: Improving mathematical accuracy
🔗 Chain-of-Reasoning (CoR) and CPL: Game-changing techniques for generalization
🌐 Why better reasoning matters for real-world AI applications across industries

🔢 Stat highlight: Chain-of-Reasoning methods have improved multi-step reasoning accuracy by up to 15% in benchmark tasks.

🔗 Ref:

https://www.microsoft.com/en-us/research/blog/new-methods-boost-reasoning-in-small-and-large-language-models/

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