GitLab

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 a number of standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of specialists (MoE) model recently open-sourced by DeepSeek. This base design is fine-tuned utilizing 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 designs and launched a number of versions of each; these models outperform larger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step towards enhancing language design reasoning abilities utilizing pure reinforcement learning (RL). Our goal is to check out the capacity of LLMs to establish reasoning abilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of jobs, consisting of imaginative writing, general question answering, wiki.whenparked.com editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on tasks needing long-context understanding, demo.qkseo.in considerably outshining DeepSeek-V3 on long-context .

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This design shows strong thinking efficiency, but" effective thinking habits, it faces several concerns. For example, DeepSeek-R1-Zero battles with challenges like poor readability and language mixing."

To resolve this, the team used a short stage of SFT to avoid the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT information using rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a range of thinking, mathematics, and coding standards and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the benchmarks, consisting of AIME 2024 and pipewiki.org MATH-500.

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

Within a few days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison blogged about his explores among the DeepSeek distilled Llama designs on his blog site:

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

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

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

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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