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DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI’s O1 Model

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI’s o1 model on a number of standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous versions of each; these designs outshine larger designs, including GPT-4, wiki.myamens.com on math and coding standards.

[DeepSeek-R1 is] the initial step toward improving language model reasoning capabilities using pure support knowing (RL). Our objective is to explore the capacity of LLMs to develop thinking capabilities without any monitored data, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … excels in a large range of jobs, including creative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on jobs requiring long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This model displays strong reasoning efficiency, however” effective reasoning habits, it faces several concerns. For instance, DeepSeek-R1-Zero has a hard time with difficulties like poor readability and language mixing.”

To resolve this, the team used a short phase of SFT to avoid the “cold start” issue of RL. They collected a number of thousand it-viking.ch examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then gathered more SFT data using rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a variety of thinking, math, and coding standards and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the standards, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also connected for # 1 with o1 in “Hard Prompt with Style Control” category.

Django framework co-creator Simon Willison wrote about his explores one of the DeepSeek distilled Llama designs on his blog:

Each reaction starts with a … pseudo-XML tag containing the chain of idea utilized to assist create the action. [Given the timely] “a joke about a pelican and a walrus who run a tea room together” … It then thought for 20 paragraphs before outputting the joke! … [T] he joke is dreadful. But the process of arriving was such a into how these new models work.

Andrew Ng’s newsletter The Batch blogged about DeepSeek-R1:

DeepSeek is quickly emerging as a strong contractor of open models. Not only are these designs great entertainers, but their license allows usage of their outputs for distillation, hb9lc.org possibly pushing forward the cutting-edge for language designs (and hb9lc.org multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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