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

DeepSeek-R1 is based on 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 variation of RL. The research study group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these models outshine larger models, consisting of GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step towards enhancing language design thinking abilities using pure support learning (RL). Our goal is to check out the capacity of LLMs to develop thinking abilities without any monitored information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of tasks, consisting of imaginative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on jobs requiring long-context understanding, substantially exceeding DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and wavedream.wiki without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This model shows strong reasoning performance, but" powerful thinking behaviors, it faces several problems. For circumstances, DeepSeek-R1-Zero has a hard time with obstacles like poor readability and language blending."

To resolve this, the group used a short 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 procedure assembled, they then gathered more SFT data utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their model on a variety of reasoning, mathematics, and coding benchmarks 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 announced 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 structure co-creator Simon Willison discussed his experiments with one of the DeepSeek distilled Llama designs on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea utilized to assist generate the reaction. [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 process of arriving was such an intriguing insight into how these new models work.

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

DeepSeek is rapidly emerging as a strong home builder of open models. Not just are these designs excellent entertainers, but their license permits use 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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