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

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

[DeepSeek-R1 is] the initial step toward improving language model reasoning capabilities using pure reinforcement knowing (RL). Our objective is to check out the capacity of LLMs to establish thinking abilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, including imaginative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks requiring long-context understanding, significantly surpassing DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also released. This model exhibits strong reasoning efficiency, but" powerful reasoning habits, it deals with several problems. For circumstances, DeepSeek-R1-Zero struggles with obstacles like poor readability and language blending."

To resolve this, the team utilized a brief stage of SFT to prevent the "cold start" problem of RL. They collected a number of thousand wiki.asexuality.org examples of chain-of-thought thinking to use 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 used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their model on a variety of reasoning, math, and coding criteria and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on several 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 mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

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

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to help create the response. [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 horrible. But the process of arriving was such a fascinating insight into how these new models work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

DeepSeek is rapidly becoming a strong builder of open models. Not only are these models great entertainers, but their license allows use of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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