DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of professionals (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 group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous versions of each; these designs outperform bigger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step towards enhancing language design thinking abilities utilizing pure support learning (RL). Our objective is to check out the capacity of LLMs to establish reasoning capabilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a broad range of tasks, including imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on tasks requiring long-context understanding, considerably outshining DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This design shows strong thinking performance, but" effective thinking behaviors, it faces several concerns. For instance, DeepSeek-R1-Zero battles with difficulties like bad readability and language mixing."

To resolve this, the group utilized a brief stage of SFT to avoid the "cold start" problem of RL. They gathered several 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 information using rejection sampling, forum.pinoo.com.tr resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their design on a variety of reasoning, mathematics, and coding benchmarks and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the benchmarks, 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 general 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 blogged about his experiments with one of the DeepSeek distilled Llama designs on his blog:

Each action starts with a ... pseudo-XML tag containing the chain of idea utilized to help generate the reaction. [Given the timely] "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 procedure of getting there was such a fascinating insight into how these new models work.

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

DeepSeek is quickly emerging as a of open models. Not just are these designs excellent entertainers, but their license allows use of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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