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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on numerous criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) model recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a version of RL. The research team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched numerous variations of each; these models exceed larger designs, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step toward enhancing language design reasoning capabilities using pure support knowing (RL). Our goal is to check out the potential of LLMs to develop thinking abilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide range of tasks, including creative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on jobs needing long-context understanding, substantially exceeding DeepSeek-V3 on long-context benchmarks.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also released. This design shows strong thinking efficiency, but" powerful thinking behaviors, it deals with several concerns. For example, DeepSeek-R1-Zero has a hard time with obstacles like poor readability and language blending."

To resolve this, the team utilized a short 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 converged, they then collected more SFT information 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 assessed their model on a variety of thinking, math, 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 criteria, 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 announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and math. It was likewise connected for # 1 with o1 in "Hard Prompt with Style Control" category.

Django framework co-creator Simon Willison composed about his experiments with among the DeepSeek distilled Llama models on his blog site:

Each reaction starts with a ... pseudo-XML tag containing the chain of idea utilized to help generate the action. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. But the procedure of arriving was such an intriguing insight into how these 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 just are these models great entertainers, setiathome.berkeley.edu however their license permits use of their outputs for distillation, possibly pushing forward the state of the art for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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