GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning ability. DeepSeek-R1 attains outcomes 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 version of RL. The research study group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous versions of each; these designs outshine larger designs, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the initial step toward enhancing language model reasoning abilities utilizing pure support learning (RL). Our objective is to explore the capacity of LLMs to develop reasoning abilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, consisting of innovative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks needing long-context understanding, considerably exceeding DeepSeek-V3 on long-context standards.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and pipewiki.org with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This model shows strong thinking performance, however" effective thinking behaviors, it deals with several issues. For example, DeepSeek-R1-Zero fights with obstacles like poor readability and language mixing."

To resolve this, the group used a brief phase of SFT to prevent the "cold start" problem of RL. They gathered numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT data utilizing 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 examined their model on a variety of reasoning, mathematics, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several of the criteria, consisting of AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a few days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each response starts with a ... pseudo-XML tag containing the chain of idea utilized to help generate the response. [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 terrible. But the procedure of getting there was such an intriguing insight into how these brand-new designs work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

DeepSeek is quickly becoming a strong home builder of open models. Not only are these designs excellent entertainers, however their license allows use of their outputs for distillation, potentially pressing forward the cutting-edge 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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