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 several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of professionals (MoE) model just recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several versions of each; these designs exceed bigger models, consisting of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the very first step toward improving language design thinking capabilities using pure reinforcement learning (RL). Our goal is to check out the potential of LLMs to establish reasoning abilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, consisting of innovative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding performance on jobs needing long-context understanding, significantly outshining DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also launched. This design exhibits strong reasoning efficiency, but" effective reasoning habits, it deals with a number of problems. For example, DeepSeek-R1-Zero fights with obstacles like poor readability and language mixing."

To resolve this, setiathome.berkeley.edu the group used a brief stage of SFT to avoid the "cold start" problem of RL. They gathered 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 collected more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and garagesale.es Qwen.

DeepSeek examined their design on a variety of reasoning, math, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded 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, 89u89.com the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison composed about his try outs one of the Llama designs on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of thought used to help generate the reaction. [Given the prompt] "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 dreadful. But the procedure of arriving was such an intriguing insight into how these new models 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 designs fantastic entertainers, however their license permits usage of their outputs for distillation, possibly pushing 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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Anthony Alford

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