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

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of professionals (MoE) model recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous variations of each; these designs exceed bigger models, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards improving language model thinking capabilities using pure support learning (RL). Our objective is to explore the capacity of LLMs to develop thinking capabilities without any monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of tasks, including innovative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), a model called DeepSeek-R1-Zero, which they have likewise released. This design displays strong reasoning performance, however" effective reasoning behaviors, it deals with numerous issues. For instance, DeepSeek-R1-Zero has problem with obstacles like poor readability and language blending."

To address this, the group used a brief stage of SFT to avoid the "cold start" problem of RL. They gathered numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT information using rejection sampling, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a range of reasoning, math, and coding standards and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed 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 couple of 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 connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django structure co-creator wavedream.wiki Simon Willison composed about his experiments with one of the DeepSeek distilled Llama models on his blog:

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to help produce the action. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is terrible. But the process of getting there was such a fascinating insight into how these new designs work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not just are these designs great entertainers, however their license allows usage of their outputs for distillation, setiathome.berkeley.edu possibly 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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