GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) model just 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 team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released several variations of each; these designs surpass larger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step toward enhancing language design thinking capabilities using pure support learning (RL). Our goal is to check out the potential of LLMs to develop thinking abilities without any monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide range of tasks, including creative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on tasks requiring long-context understanding, considerably outperforming DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek started 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 also released. This model displays strong thinking efficiency, but" powerful thinking habits, it faces numerous problems. For example, DeepSeek-R1-Zero has a hard time with obstacles like bad readability and language blending."

To address this, the group used a short stage of SFT to avoid the "cold start" issue 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 procedure assembled, trademarketclassifieds.com they then gathered more SFT information utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

their model on a variety of reasoning, mathematics, and coding benchmarks and compared it to other designs, including Claude-3.5- Sonnet, systemcheck-wiki.de GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several of the standards, consisting of 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 general in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison wrote about his try outs one of the DeepSeek distilled Llama models on his blog site:

Each reaction begins with a ... pseudo-XML tag containing the chain of idea utilized to help produce 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 horrible. But the process of arriving was such an intriguing insight into how these new designs work.

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

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

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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