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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of specialists (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team also performed understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released numerous variations of each; these models outshine larger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the first step toward enhancing language model thinking capabilities utilizing pure reinforcement learning (RL). Our objective is to explore the capacity of LLMs to establish reasoning abilities with no monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of jobs, higgledy-piggledy.xyz consisting of creative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on tasks needing long-context understanding, significantly surpassing DeepSeek-V3 on .

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This model shows strong reasoning performance, but" powerful reasoning behaviors, it deals with numerous issues. For circumstances, DeepSeek-R1-Zero fights with challenges like bad readability and language blending."

To address this, the group used a short phase of SFT to prevent the "cold start" problem of RL. They gathered several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered 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 models from Llama and Qwen.

DeepSeek assessed their design on a variety of reasoning, mathematics, and coding standards and compared it to other designs, 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 couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each action starts with a ... pseudo-XML tag containing the chain of thought used to help produce the reaction. [Given the timely] "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 terrible. But the procedure of arriving was such an intriguing insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong contractor of open models. Not just are these designs excellent entertainers, but their license allows use 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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