DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI’s O1 Model

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve thinking ability. DeepSeek-R1 attains results on par with OpenAI’s o1 model on several standards, larsaluarna.se including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of specialists (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and systemcheck-wiki.de Llama models and launched numerous variations of each; these models exceed bigger models, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the first step toward improving language model thinking abilities using pure support learning (RL). Our goal is to check out the potential of LLMs to develop reasoning abilities without any supervised information, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … excels in a wide variety of jobs, including imaginative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on tasks needing long-context understanding, substantially outperforming DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also launched. This model displays strong thinking performance, however” powerful thinking behaviors, it faces numerous concerns. For example, DeepSeek-R1-Zero has problem with difficulties like bad readability and language blending.”

To resolve this, the team used a short stage of SFT to prevent the “cold start” problem of RL. They gathered a number of thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT information 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 examined their design on a variety of reasoning, mathematics, and and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the criteria, consisting of AIME 2024 and MATH-500.

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

Within a few days of its release, hb9lc.org the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was likewise connected for # 1 with o1 in “Hard Prompt with Style Control” classification.

Django structure co-creator Simon Willison discussed 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 thought used to help generate the action. [Given the prompt] “a joke about a pelican and a walrus who run a tea space together” … It then believed for 20 paragraphs before outputting the joke! … [T] he joke is terrible. But the procedure of arriving was such an interesting insight into how these new designs work.

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

DeepSeek is quickly emerging as a strong home builder of open designs. Not only are these designs fantastic entertainers, however their license permits use of their outputs for distillation, potentially pushing forward the cutting-edge for pipewiki.org 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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