GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several benchmarks, 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 model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous versions of each; these models outperform bigger designs, consisting of GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step toward improving language model thinking capabilities utilizing pure support learning (RL). Our goal is to explore the potential of LLMs to develop thinking abilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a broad variety of jobs, consisting of innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks needing long-context understanding, significantly outshining DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This design displays strong thinking efficiency, however" effective thinking behaviors, it faces numerous issues. For instance, DeepSeek-R1-Zero fights with challenges like poor readability and language blending."

To address this, the group utilized a short phase of SFT to avoid the "cold start" problem of RL. They collected several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information utilizing rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of reasoning, math, and coding criteria and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed 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 overall 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 blogged about his explores among the DeepSeek distilled Llama models on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of idea used to help generate the reaction. [Given the prompt] "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 horrible. But the process of arriving was such an intriguing insight into how these new models work.

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

DeepSeek is rapidly emerging as a strong home builder of open models. Not just are these designs great entertainers, however their license permits use of their outputs for distillation, possibly 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

Rate this Article

This content remains in the AI, ML & Data Engineering topic

Related Topics:

- AI, pediascape.science ML & Data Engineering

• Generative AI
• Large language models
  
  - Related Editorial
  
  Related Sponsored Content
  
  - [eBook] Getting Started with Azure Kubernetes Service
  
  Related Sponsor
  
  Free services for AI apps. Are you ready to explore innovative innovations? You can begin building intelligent apps with complimentary Azure app, data, and AI services to minimize in advance expenses. Discover more.
  
  How could we enhance? Take the InfoQ reader study
  
  Each year, we seek feedback from our readers to assist us improve InfoQ. Would you mind costs 2 minutes to share your feedback in our short study? Your feedback will straight help us continually progress how we support you. The InfoQ Team Take the survey
  
  Related Content
  
  The InfoQ Newsletter
  
  A round-up of recently's content on InfoQ sent out every Tuesday. Join a community of over 250,000 senior designers.