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

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on numerous standards, including MATH-500 and pediascape.science SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several versions of each; these designs surpass bigger models, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards enhancing language model reasoning abilities utilizing pure support learning (RL). Our goal is to check out the potential of LLMs to develop reasoning capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, consisting of imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks needing understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually likewise launched. This model shows strong thinking performance, but" powerful reasoning habits, it faces numerous problems. For example, DeepSeek-R1-Zero has problem with obstacles like bad readability and language mixing."

To resolve this, the team used a brief stage of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a variety of thinking, mathematics, and coding benchmarks and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on numerous of the benchmarks, 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 likewise tied 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 site:

Each action starts with a ... pseudo-XML tag containing the chain of idea used to assist produce the reaction. [Given the timely] "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 awful. But the process of getting there was such an intriguing insight into how these brand-new designs work.

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

DeepSeek is rapidly emerging as a strong builder of open designs. Not only are these models terrific entertainers, but their license permits 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] Beginning with Azure Kubernetes Service

Related Sponsor

Free services for AI apps. Are you ready to try out innovative technologies? You can begin developing intelligent apps with complimentary Azure app, information, and AI services to reduce upfront expenses. Learn More.

How could we improve? Take the InfoQ reader survey

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 survey? Your feedback will straight assist us continuously evolve how we support you. The InfoQ Team Take the study

Related Content

The InfoQ Newsletter

A round-up of last week's material on InfoQ sent every Tuesday. Join a neighborhood of over 250,000 senior developers.