GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to improve reasoning capability. 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 on DeepSeek-V3, a mixture of professionals (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched numerous versions of each; these designs outshine bigger models, including GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the very first step toward improving language model reasoning abilities using pure reinforcement learning (RL). Our goal is to explore the potential of LLMs to develop reasoning abilities with no monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, including creative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on jobs needing long-context understanding, significantly outperforming DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek started 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 launched. This design shows strong thinking performance, but" powerful reasoning habits, it faces a number of issues. For example, DeepSeek-R1-Zero battles with challenges like poor readability and language blending."

To resolve this, the team used a short phase of SFT to prevent the "cold start" issue of RL. They gathered a number of thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

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

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

Within a couple of days of its release, the that DeepSeek-R1 was ranked # 3 total in the arena and wiki.dulovic.tech # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison blogged about his explores among the DeepSeek distilled Llama models on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of thought utilized to assist produce the response. [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 horrible. But the procedure of getting there was such a fascinating insight into how these new models work.

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

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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