DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several criteria, including 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 design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of versions of each; these designs exceed larger models, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the very first action toward enhancing language design thinking abilities using pure support learning (RL). Our goal is to check out the potential of LLMs to establish reasoning abilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of jobs, consisting of creative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on tasks needing long-context understanding, considerably outshining DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just 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 reasoning efficiency, however" powerful thinking behaviors, it faces several problems. For circumstances, DeepSeek-R1-Zero battles with obstacles like poor readability and language mixing."

To address this, the team utilized a brief stage of SFT to prevent the "cold start" issue of RL. They gathered numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT data using rejection tasting, leading to 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 model on a variety of thinking, mathematics, disgaeawiki.info and coding standards 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 revealed that DeepSeek-R1 was ranked # 3 total in the arena and # 1 in coding and mathematics. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison discussed his try outs among the DeepSeek distilled Llama models on his blog:

Each response starts with a ... pseudo-XML tag containing the chain of thought used to assist generate the action. [Given the prompt] "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 horrible. But the procedure of arriving was such an interesting insight into how these brand-new designs work.

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

DeepSeek is quickly emerging as a strong home builder of open models. Not just are these designs fantastic entertainers, setiathome.berkeley.edu however their license permits use of their outputs for distillation, potentially pressing forward the cutting-edge for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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