GitLab

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

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of professionals (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and disgaeawiki.info released a number of versions of each; these designs outperform larger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step toward enhancing language design thinking capabilities using pure reinforcement learning (RL). Our objective is to explore the potential of LLMs to establish thinking capabilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, including creative writing, surgiteams.com basic question answering, modifying, summarization, forum.batman.gainedge.org and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on jobs requiring long-context understanding, significantly surpassing DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise released. This design displays strong reasoning performance, however" effective thinking habits, it deals with several issues. For example, DeepSeek-R1-Zero has problem with difficulties like poor readability and language blending."

To address this, the team used a brief phase of SFT to prevent 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 gathered more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their design on a variety of thinking, math, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed 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 few days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and hb9lc.org # 1 in coding and mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django structure co-creator yewiki.org Simon Willison composed about his explores one of the DeepSeek distilled Llama designs on his blog site:

Each response starts with a ... pseudo-XML tag containing the chain of thought utilized to assist create the action. [Given the prompt] "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 intriguing insight into how these new models work.

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

DeepSeek is rapidly becoming a strong builder of open models. Not just are these designs fantastic entertainers, setiathome.berkeley.edu however their license allows usage of their outputs for higgledy-piggledy.xyz distillation, potentially pressing forward the cutting-edge for (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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