GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team likewise performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched a number of versions of each; these models surpass larger designs, consisting of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the primary step towards improving language design thinking capabilities utilizing pure reinforcement knowing (RL). Our objective is to check out the potential of LLMs to develop thinking capabilities with no monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large variety of tasks, including creative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks requiring long-context understanding, significantly outperforming DeepSeek-V3 on long-context standards.

To develop the model, pediascape.science DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This model displays strong reasoning efficiency, but" powerful reasoning habits, it deals with several concerns. For example, DeepSeek-R1-Zero has problem with challenges like poor readability and language blending."

To address this, the group utilized a short phase of SFT to prevent the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT data using rejection tasting, resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their model on a range of reasoning, math, and coding benchmarks and compared it to other models, of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the benchmarks, including 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 general in the arena and # 1 in coding and math. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each reaction starts with a ... pseudo-XML tag containing the chain of thought used to assist create the action. [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 dreadful. 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 becoming a strong home builder of open models. Not just are these designs great entertainers, however their license allows usage of their outputs for distillation, potentially pressing forward the state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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