DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on numerous benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of specialists (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several variations of each; these designs surpass larger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step toward improving language design reasoning abilities using pure support learning (RL). Our objective is to explore the potential of LLMs to develop thinking capabilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, including imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on jobs needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, trademarketclassifieds.com and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This design displays strong reasoning performance, however" effective thinking habits, it faces several concerns. For circumstances, DeepSeek-R1-Zero has problem with difficulties like bad readability and language blending."

To address this, the group utilized a brief stage of SFT to prevent the "cold start" issue of RL. They collected numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT data utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their design on a range of reasoning, mathematics, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous of the standards, consisting of 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 total 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 models on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea used to help create the reaction. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of arriving was such an intriguing insight into how these new designs work.

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

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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