GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, a mixture of specialists (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous variations of each; these models exceed bigger designs, including GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the initial step toward improving language model reasoning abilities utilizing pure reinforcement knowing (RL). Our goal is to explore the capacity of LLMs to establish reasoning capabilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, including imaginative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on tasks needing long-context understanding, considerably outperforming DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They first attempted 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 shows efficiency, however" powerful reasoning habits, it deals with numerous issues. For example, DeepSeek-R1-Zero has a hard time with obstacles like poor readability and language mixing."

To address this, the group used a short stage of SFT to avoid the "cold start" issue of RL. They collected numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT information utilizing rejection tasting, yewiki.org resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their design on a range of reasoning, mathematics, and oeclub.org coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the criteria, 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 mathematics. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django structure co-creator Simon Willison discussed his explores among the DeepSeek distilled Llama designs on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of idea used to help generate the reaction. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is dreadful. But the procedure of arriving was such an interesting insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong builder of open designs. Not only are these models excellent entertainers, wavedream.wiki but their license permits use of their outputs for distillation, potentially pressing forward the state of the art for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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