Author: zddeepseeker

Today we will share DeepSeek R1, Title: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning: Incentivizing the reasoning capability of LLM via reinforcement learning. This paper introduces DeepSeek’s first generation of reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. The DeepSeek-R1-Zero model was trained through large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as an initial step,…

1 Background During the Spring Festival, DeepSeek R1 once again attracted widespread attention, and even the DeepSeek V3 interpretation article we previously wrote was also re-transmitted and discussed a lot. Although there have been many analyses and reproductions of DeepSeek R1, here we have decided to compile some corresponding reading notes. We will use three…

Highlights Competition with DeepSeek is good for the US Cathie Wood: I think it shows that the cost of innovation is falling dramatically, and that this trend has already started. For example, before DeepSeek, the cost of training artificial intelligence fell by 75% per year, and the cost of inference even fell by 85% to…

The story of Gemini 2.0 is accelerating. The Flash Thinking Experimental version in December brought developers a working model with low latency and high performance. Earlier this year, 2.0 Flash Thinking Experimental was updated in the Google AI Studio to further improve performance by combining the speed of Flash with enhanced inference capabilities. Last week,…

Just now, another domestic model was added to the Big Model Arena list from Ali, Qwen2.5-Max, which surpassed DeepSeek-V3 and ranked seventh in the overall rankings with a total score of 1332. It also surpassed models such as Claude 3.5 Sonnet and Llama 3.1 405B in one fell swoop. In particular, it excels in programming…