GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking 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 on DeepSeek-V3, a mix of specialists (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), larsaluarna.se a reasoning-oriented version of RL. The research group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and wakewiki.de Llama models and released numerous versions of each; these designs surpass larger models, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the initial step toward improving language design thinking capabilities utilizing pure reinforcement knowing (RL). Our objective is to explore the potential of LLMs to develop reasoning abilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of tasks, including innovative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on jobs requiring long-context understanding, significantly outshining DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This model shows strong thinking efficiency, but" powerful reasoning behaviors, it deals with several problems. For instance, DeepSeek-R1-Zero battles with challenges 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 a number of thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT information utilizing rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and hb9lc.org to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their model on a variety of thinking, mathematics, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the standards, 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 overall in the arena and # 1 in coding and math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison discussed his explores one of the DeepSeek distilled Llama models on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought utilized to assist generate the action. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of getting there was such an interesting insight into how these new designs work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not just are these models fantastic entertainers, but their license allows use of their outputs for distillation, potentially pushing forward the state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

Rate this Article

This material remains in the AI, wiki.myamens.com ML & Data Engineering topic

Related Topics:

- AI, ML & Data Engineering

• Generative AI
• Large language designs
  
  - Related Editorial
  
  Related Sponsored Content
  
  - [eBook] Starting with Azure Kubernetes Service
  
  Related Sponsor
  
  Free services for AI apps. Are you prepared to try out innovative innovations? You can begin developing intelligent apps with free Azure app, data, and AI services to decrease upfront expenses. Learn More.
  
  How could we enhance? Take the InfoQ reader study
  
  Each year, we seek feedback from our readers to assist us enhance InfoQ. Would you mind spending 2 minutes to share your feedback in our short study? Your feedback will straight help us continuously evolve how we support you. The InfoQ Team Take the study
  
  Related Content
  
  The InfoQ Newsletter
  
  A round-up of recently's content on InfoQ sent out every Tuesday. Join a neighborhood of over 250,000 senior developers.