DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on several criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research study team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released several variations of each; these designs surpass larger designs, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step toward improving language model reasoning abilities using pure reinforcement knowing (RL). Our goal is to check out the capacity of LLMs to develop reasoning capabilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a broad variety of jobs, consisting of imaginative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on tasks needing long-context understanding, substantially surpassing DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, hb9lc.org and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This model shows strong reasoning performance, however" powerful thinking behaviors, it faces several issues. For circumstances, DeepSeek-R1-Zero has problem with obstacles like poor readability and language mixing."

To resolve this, the group used a brief stage of SFT to avoid the "cold start" issue of RL. They gathered numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the converged, they then gathered more SFT data using rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model on a range of reasoning, math, and coding standards and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined 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 announced that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison blogged about his try outs one of the DeepSeek distilled Llama designs on his blog site:

Each response begins with a ... pseudo-XML tag containing the chain of idea utilized to help create the action. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of arriving was such a fascinating insight into how these brand-new designs work.

Andrew Ng's newsletter The Batch wrote about DeepSeek-R1:

DeepSeek is rapidly emerging as a strong builder of open models. Not only are these models excellent entertainers, however their license permits use of their outputs for distillation, possibly pressing forward the cutting-edge for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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