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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to enhance reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of professionals (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Optimization (GRPO), a reasoning-oriented variant of RL. The research team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of versions of each; these designs outperform bigger designs, oeclub.org consisting of GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the initial step towards enhancing language design reasoning capabilities using pure reinforcement learning (RL). Our goal is to check out the capacity of LLMs to develop reasoning capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, consisting of innovative writing, basic question answering, setiathome.berkeley.edu editing, summarization, and disgaeawiki.info more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks requiring long-context understanding, significantly outperforming DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, wavedream.wiki and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This design shows strong thinking performance, but" powerful reasoning behaviors, it faces numerous issues. For instance, DeepSeek-R1-Zero fights with challenges like poor readability and language mixing."

To resolve this, the group utilized a short phase of SFT to avoid the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then gathered more SFT data utilizing rejection sampling, resulting in a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a range of reasoning, mathematics, and coding criteria 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 benchmarks, including 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 total in the arena and # 1 in coding and math. It was also tied for higgledy-piggledy.xyz # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his experiments with among 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 action. [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 process of getting there was such a fascinating insight into how these new designs work.

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

DeepSeek is rapidly emerging as a strong builder of open designs. Not just are these models terrific entertainers, however their license allows usage of their outputs for distillation, possibly pushing forward the cutting-edge for language models (and higgledy-piggledy.xyz multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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