GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on numerous criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched numerous variations of each; these designs surpass bigger designs, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the primary step toward enhancing language model reasoning abilities utilizing pure reinforcement learning (RL). Our objective is to explore the potential of LLMs to develop thinking abilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a vast array of jobs, including innovative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on tasks needing long-context understanding, substantially exceeding DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise released. This design shows strong reasoning performance, but" powerful thinking behaviors, it faces numerous problems. For example, DeepSeek-R1-Zero fights with difficulties like bad readability and language blending."

To address this, the team used a brief phase of SFT to prevent the "cold start" issue of RL. They gathered numerous thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information utilizing rejection tasting, trademarketclassifieds.com resulting in a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a range of reasoning, math, and 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, 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 revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

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

Each action begins with a ... pseudo-XML tag containing the chain of idea utilized to assist produce 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 getting there was such an interesting insight into how these brand-new designs work.

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

DeepSeek is quickly becoming a strong home builder of open models. Not only are these models excellent entertainers, but their license allows usage of their outputs for distillation, potentially pushing 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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