# Jan 26, 2025

### Daily Updates

- **DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning**  
  **DeepSeek-R1** models, including **DeepSeek-R1-Zero**, leverage **large-scale reinforcement learning** to exhibit advanced reasoning capabilities, though they face challenges like *poor readability* and *language mixing*.

- **Qwen2.5-1M: Deploy your own Qwen with context length up to 1M tokens**  
  **Qwen2.5-1M models** introduce **two new checkpoints** capable of processing **1 million tokens**, significantly enhancing long-context capabilities compared to previous versions.

- **7B Model and 8K Examples: Efficient and Effective Emerging Reasoning with RL**  
  **Notion** serves as a **comprehensive workspace** that integrates notes, tasks, wikis, and databases, enhancing productivity through its versatile features and user-friendly interface.

- **Explainer: What's R1 and Everything Else?**  
  **R1 is an open-source reasoning model** that matches the performance of OpenAI's o1 while being **30x cheaper**, providing a significant alternative for AI development and deployment.

- **Using the most unhinged AVX-512 instruction to make fastest phrase search algo**  
  **AVX-512 instruction** enables a **phrase search algorithm** that outperforms Meilisearch by up to **1600x**, showcasing significant advancements in computational efficiency for phrase searches in large datasets. The source code is available [here](https://github.com/Gab-Menezes/simdphrase) and the benchmarking methodology is detailed in the article.

- **Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M**  
  **Qwen 2.5 LLM** now supports a **context length of up to 1 million tokens**, a significant increase from the previous limit of 128,000 tokens, enabled by the innovative **Dual Chunk Attention** technique detailed in [this paper](https://arxiv.org/abs/2402.17463).

- **Using AI to develop a fuller model of the human brain**  
  **Silicon brains** may revolutionize neuroscience by mimicking human cognitive functions, potentially leading to breakthroughs in understanding brain aging and neurodegenerative diseases.

- **Schrödinger: The Nvidia biotech partner Jensen Huang told to "think bigger"**  
  **Schrödinger** leverages **quantum mechanics** and **AI** to revolutionize drug discovery, with all top 20 pharmaceutical companies as clients, and significant revenue from partnerships, including **$111 million** from Takeda and **$47.6 million** from Eli Lilly.

- **The impact of competition and DeepSeek on Nvidia**  
  **Nvidia's stock faces significant challenges** as competition intensifies from innovative architectures like Cerebras and Groq, which threaten its data center dominance and high margins, while major customers develop custom silicon to reduce reliance on Nvidia's products.

- **Mastering Atari Games with Natural Intelligence**  
  **Genius™-Powered Agents** demonstrate superior performance in Atari games, achieving human-level and perfect play with **90% less data** than traditional models, marking a significant advancement in sample efficiency and learning speed.

- **Silicon-Photonic Accelerator Design for Efficient GAN Computation and Energy Reduction**  
  The **silicon photonic accelerator** designed for **GAN operations** replaces traditional electronic computation with light-based processing, achieving a **4.4x speedup** and **2.18x energy reduction** compared to GPUs/TPUs.

- **Replicating DeepSeek-R3-Zero RL recipe on 3B LLM for <30$, the model develops self-verification and search abilities all on its own**  
  **Replicating DeepSeek-R3-Zero** on a **3B LLM** for under **$30** demonstrates that **reinforcement learning (RL)** can effectively enhance language models, enabling them to develop **self-verification** and **search abilities** autonomously.