GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a of RL. The research group likewise performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several variations of each; these models outperform larger designs, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the primary step towards improving language design thinking abilities utilizing pure reinforcement learning (RL). Our objective is to explore the capacity of LLMs to develop reasoning abilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a vast array of tasks, including innovative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional performance on jobs needing long-context understanding, considerably surpassing DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek began 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 also released. This design exhibits strong thinking efficiency, however" powerful thinking habits, it faces several concerns. For example, DeepSeek-R1-Zero battles with challenges like bad readability and language blending."

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

DeepSeek assessed their model on a variety of reasoning, mathematics, and coding benchmarks 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 standards, including 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" classification.

Django framework co-creator Simon Willison blogged about his explores one of the DeepSeek distilled Llama models on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of idea utilized to assist create the response. [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 terrible. But the process of getting there was such an intriguing insight into how these brand-new designs work.

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

DeepSeek is rapidly becoming a strong contractor of open models. Not only are these models terrific entertainers, but their license permits use of their outputs for disgaeawiki.info distillation, possibly pressing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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