GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on a number of standards, including MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the primary step toward improving language model reasoning capabilities utilizing pure support knowing (RL). Our objective is to explore the capacity of LLMs to develop reasoning capabilities with no 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 question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on tasks requiring long-context understanding, significantly outshining DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This design shows strong reasoning performance, however" powerful reasoning habits, it deals with several concerns. For example, DeepSeek-R1-Zero fights with challenges like bad readability and language blending."

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

DeepSeek evaluated their design on a range of thinking, mathematics, and coding criteria and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several 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 total in the arena and # 1 in coding and mathematics. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

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

Each response begins with a ... pseudo-XML tag containing the chain of thought utilized to help generate the response. [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 awful. But the process of arriving was such a fascinating insight into how these brand-new models work.

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

DeepSeek is rapidly emerging as a strong home builder of open designs. Not only are these designs fantastic entertainers, but their license allows 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 models are available on HuggingFace.

About the Author

Anthony Alford

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This material remains in the AI, setiathome.berkeley.edu ML & Data Engineering subject

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