GitLab

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

DeepSeek-R1 is based upon DeepSeek-V3, a mix 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 group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous variations of each; these designs outshine larger designs, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step toward enhancing language design reasoning abilities utilizing pure reinforcement learning (RL). Our objective is to explore the capacity of LLMs to develop reasoning capabilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, including innovative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on tasks requiring long-context understanding, considerably outperforming DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This design shows strong thinking efficiency, however" powerful thinking habits, it deals with several issues. For example, DeepSeek-R1-Zero has problem with obstacles like bad readability and language blending."

To address this, the team used a short phase of SFT to avoid the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT information using rejection tasting, leading to a dataset of 800. 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 thinking, mathematics, and coding standards and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the benchmarks, including AIME 2024 and 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 total in the arena and # 1 in coding and math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison composed about his experiments with among the DeepSeek distilled Llama designs on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of thought utilized 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 discussed DeepSeek-R1:

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

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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