reasoning

AI models, like Claude 3.7 Sonnet, misrepresent their capabilities as “reasoning engines.” Originally designed for language processing, the technology has been rebranded despite fundamental limitations revealed by research. Models exhibit pattern-matching rather than true reasoning, facing challenges like inconsistent results and heavy token costs for minimal user benefit. This marketing-driven narrative distorts public perception while failing to deliver practical applications. Consequently, it results in significant misconceptions, misguided investments, and a disconnect between AI's marketed potential and actual performance. Transparency and realistic expectations are vital for future AI development.

https://ai-cosmos.hashnode.dev/the-illusion-of-reasoning-unmasking-the-reality-of-reasoning-models-like-claude-37-sonnet

Open Reasoning Data includes 1,748,344 questions and 300,119 thought traces for model training, categorized into various subjects like Mathematics, Medical, Chemistry, Physics, Biology, Languages, Engineering, Social Sciences, Humanities, and Coding, with detailed stats on questions and traces for each category.

https://gr.inc/

TLDR: The article by Sebastian Raschka discusses reasoning models in large language models (LLMs), focusing on four approaches for enhancement: inference-time scaling, pure reinforcement learning (RL), supervised fine-tuning combined with RL, and model distillation. It distinguishes reasoning tasks that need intermediate steps, explains the development pipeline of DeepSeek's R1 models, and offers insights into efficient practices for building these models, even on a limited budget. Key takeaways include the emergence of reasoning through RL and the effectiveness of model distillation. Future trends involve combining RL with inference-time scaling for improved performance.

https://magazine.sebastianraschka.com/p/understanding-reasoning-llms

**Extreme TLDR:** “Calling Bullshit: The Art of Skepticism in a Data-Driven World” by Carl Bergstrom and Jevin West teaches critical thinking to combat misinformation and data manipulation prevalent in politics, science, and media. It aims to help students identify and refute dubious claims, especially those disguised as rigorous scholarly work.

[https://callingbullshit.org/](https://callingbullshit.org/)