GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to enhance reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several benchmarks, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several versions of each; these models outshine bigger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step toward enhancing language design thinking abilities utilizing pure support knowing (RL). Our objective is to check out the potential of LLMs to establish reasoning capabilities 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, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on jobs needing long-context understanding, substantially surpassing DeepSeek-V3 on .

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This model exhibits strong thinking performance, but" effective thinking behaviors, it deals with numerous problems. For instance, DeepSeek-R1-Zero battles with challenges like bad readability and language mixing."

To resolve this, the group utilized a brief stage of SFT to avoid the "cold start" issue of RL. They gathered several thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT information utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a variety of thinking, math, and coding criteria and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on several of the standards, 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 overall in the arena and # 1 in coding and it-viking.ch mathematics. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison discussed his explores among the DeepSeek distilled Llama models on his blog site:

Each reaction starts with a ... pseudo-XML tag containing the chain of idea utilized to assist produce the response. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of arriving was such an interesting insight into how these new models work.

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

DeepSeek is rapidly becoming a strong contractor of open models. Not only are these models fantastic entertainers, however their license permits usage of their outputs for distillation, potentially pushing forward the state of the art for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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