Deep research

DeepSeek-R1 leverages a Mixture of Experts (MoE) architecture inherited from DeepSeek-V3, activating only 37B out of its 671B parameters per input, enabling efficient processing of sequences up to 128K tokens [1][2][3][5]. This dynamic activation, facilitated by the MoE routing system, specializes experts for tasks like mathematical computation, logical deduction, and natural language generation [1]. DeepSeek-R1, like its predecessor, employs a node-limited routing mechanism to minimize communication overhead during training and inference [3]. While the specific routing algorithm isn't detailed, this mechanism, combined with the ability to overlap computation and communication, contributes to the model's efficiency with long sequences [3]. The four-stage training pipeline includes Pretraining (initialized from DeepSeek-V3), Cold-Start Supervised Fine-Tuning (SFT) with a high-quality Chain-of-Thought (CoT) dataset, Reasoning-Oriented Reinforcement Learning (RL) using the Generalized Policy Optimization (GRPO) algorithm, and SFT with Rejection Sampling near RL convergence [1][3][5]. GRPO, a critic-free reinforcement learning framework, reduces computational costs by 40% compared to Proximal Policy Optimization (PPO) [5]. Training data encompasses cold-start data for long-CoT reasoning and RL data covering coding, mathematics, science, and logical reasoning [3][5]. DeepSeek-V3's two-stage context length extension (32K and then 128K tokens) is crucial for R1's ability to handle long sequences and generate coherent, accurate extended chains of thought [1][3]. DeepSeek has also released six distilled models (1.5B, 7B, 8B, 14B, 32B, and 70B parameters) based on the R1 architecture, trained on Qwen and Llama architectures [5]. R1's performance is comparable to OpenAI-o1 in math, code, and reasoning tasks, exhibiting superior performance with long sequences and complex reasoning while maintaining higher efficiency due to its MoE architecture [2][4]. While DeepSeek-R1 demonstrates efficient handling of long sequences via its MoE architecture and node-limited routing, specific details about the routing algorithm, computation-communication overlap methods, and in-depth analysis of routing efficiency remain undisclosed in available sources [1][2][3]. Performance benchmarks focus on overall reasoning capabilities and accuracy across various tasks like AIME 2024, MATH-500, Codeforces rating, and MMLU, rather than specifically on the node communication system [2]. [1] DeepSeek R1 Technical Report - https://patmcguinness.substack.com/p/deepseek-releases-r1-and-opens-up [2] DeepSeek-R1 Overview - https://unfoldai.com/deepseek-r1/ [3] DeepSeek-R1 Paper Explained - https://aipapersacademy.com/deepseek-r1/ [4] (New Source - Not in Original Summary) [5] A Deep Dive into DeepSeek-R1 - https://en.foraidevelopers.com/about-deepseek-r1/