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

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of professionals (MoE) model recently open-sourced by DeepSeek. This base design is fine-tuned using 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 several variations of each; these designs outperform bigger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the very first step towards enhancing language model thinking capabilities utilizing pure reinforcement knowing (RL). Our objective is to explore the potential of LLMs to develop thinking capabilities without any monitored information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large variety of tasks, including imaginative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on jobs needing long-context understanding, substantially outshining DeepSeek-V3 on long-context standards.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually likewise launched. This model exhibits strong reasoning performance, however" powerful reasoning behaviors, it faces a number of concerns. For example, DeepSeek-R1-Zero battles with difficulties like poor readability and language blending."

To resolve this, the group used a brief stage of SFT to avoid the "cold start" problem of RL. They gathered a number of thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT information using rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to the distilled models from Llama and Qwen.

DeepSeek examined their design on a range of thinking, mathematics, setiathome.berkeley.edu and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the benchmarks, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: forum.altaycoins.com 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 composed about his try outs one of the DeepSeek distilled Llama designs on his blog:

Each action starts with a ... pseudo-XML tag containing the chain of thought used to assist generate the response. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of getting there was such an intriguing insight into how these brand-new models work.

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

DeepSeek is quickly becoming a strong home builder of open designs. Not only are these designs terrific entertainers, however their license permits use of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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- AI, setiathome.berkeley.edu ML & Data Engineering - Generative AI

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