GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support knowing (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of experts (MoE) design just 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 group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several versions of each; these models outperform bigger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the first action toward improving language design reasoning abilities utilizing pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to develop thinking abilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a broad variety of tasks, including innovative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on tasks needing long-context understanding, considerably outshining DeepSeek-V3 on long-context standards.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), bio.rogstecnologia.com.br producing a design called DeepSeek-R1-Zero, which they have also launched. This model exhibits strong thinking efficiency, but" powerful reasoning habits, it faces several concerns. For instance, DeepSeek-R1-Zero fights with difficulties like poor readability and language blending."

To resolve this, wavedream.wiki the team utilized a brief phase of SFT to avoid the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT information utilizing rejection tasting, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a variety of reasoning, math, and coding benchmarks and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the benchmarks, including 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 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 discussed his try outs one of the DeepSeek distilled Llama models on his blog:

Each action starts with a ... pseudo-XML tag containing the chain of thought used to assist produce the action. [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 horrible. But the procedure of getting there was such an intriguing insight into how these new designs work.

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

DeepSeek is rapidly emerging as a strong builder of open designs. Not only are these designs fantastic entertainers, however their license allows use of their outputs for distillation, potentially pushing forward the cutting-edge for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on .

About the Author

Anthony Alford

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