DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI’s O1 Model

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI’s o1 model on several benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and pipewiki.org launched a number of versions of each; these designs outperform larger designs, consisting of GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step towards enhancing language design reasoning capabilities using pure support knowing (RL). Our objective is to check out the capacity of LLMs to develop thinking capabilities with no monitored information, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … master a large range of jobs, including imaginative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on tasks needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise released. This model shows strong thinking performance, however” effective thinking habits, it faces numerous concerns. For example, DeepSeek-R1-Zero fights with obstacles like poor readability and language blending.”

To address this, the team utilized a brief phase of SFT to avoid the “cold start” issue of RL. They collected numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then gathered more SFT data using rejection tasting, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their model on a variety of reasoning, math, and yewiki.org 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 benchmarks, consisting of AIME 2024 and MATH-500.

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

Within a couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall 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 framework co-creator Simon Willison blogged about his try outs among the DeepSeek distilled Llama models on his blog site:

Each reaction begins with a … pseudo-XML tag containing the chain of thought used to help generate the response. [Given the prompt] “a joke about a pelican and a walrus who run a tea room together” … It then believed for 20 paragraphs before outputting the joke! … [T] he joke is terrible. But the process of getting there was such an intriguing insight into how these new models work.

Andrew Ng’s newsletter The Batch composed about DeepSeek-R1:

DeepSeek is rapidly emerging as a strong builder of open designs. Not just are these designs great entertainers, but their license allows usage of their outputs for distillation, possibly pressing forward the state of the art for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 designs 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, 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 experiment with cutting-edge innovations? You can start constructing intelligent apps with totally free Azure app, information, and AI services to minimize in advance expenses. Find out more.

How could we improve? Take the InfoQ reader study

Each year, we look for feedback from our readers to assist us improve InfoQ.
Would you mind spending 2 minutes to share your feedback in our short survey?
Your feedback will straight assist us continuously develop how we support you.
The InfoQ Team
Take the survey

Related Content

The InfoQ Newsletter

A round-up of recently’s material on InfoQ sent out every Tuesday. Join a community of over 250,000 senior surgiteams.com developers.