GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on a number of benchmarks, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) model just recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a of RL. The research group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released a number of variations of each; these designs surpass larger models, consisting of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the primary step towards improving language model thinking capabilities utilizing pure support learning (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 ... excels in a vast array of jobs, consisting of innovative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks needing long-context understanding, substantially outshining DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This design displays strong thinking performance, but" effective reasoning habits, it deals with numerous problems. For circumstances, DeepSeek-R1-Zero fights with challenges like poor readability and language blending."

To address this, the team utilized a short phase of SFT to prevent the "cold start" problem 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, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of thinking, math, and coding benchmarks and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous of the criteria, pipewiki.org including 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 trademarketclassifieds.com math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison blogged about his try outs one of the DeepSeek distilled Llama models on his blog site:

Each response begins with a ... pseudo-XML tag containing the chain of idea utilized to help create 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 an interesting insight into how these brand-new models work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

DeepSeek is rapidly emerging as a strong contractor of open models. Not just are these designs terrific entertainers, but their license permits use of their outputs for distillation, potentially pushing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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