Skip to content

GitLab

Open
Created by Claribel Ocampo@claribelocampoMaintainer

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 enhance thinking ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on several standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (MoE) model 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 study team likewise out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released a number of versions of each; these models outperform bigger models, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards enhancing language model reasoning capabilities using pure reinforcement knowing (RL). Our goal is to explore the potential of LLMs to establish reasoning capabilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of jobs, including creative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on jobs requiring long-context understanding, significantly surpassing DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This model shows strong thinking efficiency, however" effective reasoning habits, it faces a number of problems. For instance, DeepSeek-R1-Zero struggles with obstacles like bad readability and language mixing."

To address this, the group utilized a brief stage of SFT to avoid the "cold start" issue of RL. They collected a number of thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of thinking, mathematics, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on numerous of the benchmarks, including AIME 2024 and bytes-the-dust.com 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 tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison discussed his try outs one of the DeepSeek distilled Llama designs on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of thought used to assist create the reaction. [Given the timely] "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 dreadful. But the procedure of getting there was such an interesting insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong builder of open models. Not only are these designs great entertainers, but their license allows use of their outputs for distillation, potentially pushing forward the cutting-edge for language models (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, ML & Data Engineering