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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 a number of criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of professionals (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), bytes-the-dust.com a of RL. The research group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous versions of each; these designs outperform larger designs, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the primary step towards improving language design reasoning capabilities utilizing pure reinforcement knowing (RL). Our goal is to explore the capacity of LLMs to develop reasoning capabilities with no supervised information, pediascape.science concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, including imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional performance on jobs needing long-context understanding, surgiteams.com significantly outshining DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also released. This model displays strong reasoning performance, but" effective reasoning behaviors, it faces numerous issues. For example, DeepSeek-R1-Zero deals with difficulties like poor readability and language blending."

To resolve this, it-viking.ch the group utilized a short phase of SFT to prevent the "cold start" issue of RL. They gathered a number of thousand surgiteams.com examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their design on a range of reasoning, math, and coding standards and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous 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, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall 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 discussed his try outs among the DeepSeek distilled Llama models on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought utilized to help produce the reaction. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. But the procedure of getting there 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 builder of open models. Not just are these models terrific entertainers, however their license allows usage of their outputs for distillation, potentially pushing forward the state of the art for wavedream.wiki language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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