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 several standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (MoE) design just 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 group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several versions of each; these designs surpass bigger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the primary step towards improving language design thinking abilities using pure support knowing (RL). Our objective is to explore the potential of LLMs to develop reasoning abilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, consisting of imaginative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on tasks requiring long-context understanding, substantially exceeding DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, archmageriseswiki.com and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This model displays strong reasoning efficiency, but" powerful reasoning behaviors, it faces several concerns. For instance, DeepSeek-R1-Zero struggles with difficulties like poor readability and language mixing."

To address this, the team used a short stage of SFT to avoid the "cold start" problem of RL. They collected a number of thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT data using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their design on a variety of reasoning, math, and coding benchmarks and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the benchmarks, including AIME 2024 and MATH-500.

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

Within a couple of days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison blogged about his experiments with among the DeepSeek distilled Llama models on his blog site:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought utilized to help create the reaction. [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 horrible. But the procedure of arriving was such an intriguing insight into how these brand-new models work.

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

DeepSeek is quickly becoming a strong builder of open models. Not only are these designs fantastic entertainers, however their license allows usage of their outputs for distillation, possibly pressing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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