GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) design just 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 study group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of variations of each; these designs surpass larger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the first step toward enhancing language design thinking abilities utilizing pure support knowing (RL). Our goal is to check out the capacity of LLMs to develop reasoning capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, including innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on jobs requiring long-context understanding, considerably outshining DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also released. This model shows strong thinking efficiency, however" powerful thinking habits, it deals with numerous concerns. For example, DeepSeek-R1-Zero battles with obstacles like poor readability and language mixing."

To resolve this, the team utilized a short phase of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT information utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their model on a variety of thinking, wavedream.wiki math, 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 numerous of the criteria, including AIME 2024 and MATH-500.

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

Within a couple of 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 likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison blogged about his explores among the DeepSeek distilled Llama models on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of thought utilized to help create the response. [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 awful. But the process of arriving was such an interesting insight into how these brand-new designs work.

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

DeepSeek is rapidly emerging as a strong home of open designs. Not only are these models great entertainers, however their license permits use of their outputs for distillation, possibly pressing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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