DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve thinking capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on a number of benchmarks, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of professionals (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research study team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several versions of each; these models outshine larger models, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the first step towards enhancing language model reasoning abilities utilizing pure support learning (RL). Our objective is to explore the capacity of LLMs to establish thinking capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of jobs, consisting of innovative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on jobs requiring long-context understanding, significantly surpassing DeepSeek-V3 on long-context standards.

To develop the model, 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 model called DeepSeek-R1-Zero, which they have actually also released. This model shows strong thinking performance, however" powerful thinking behaviors, it deals with numerous problems. For example, DeepSeek-R1-Zero has problem with challenges like bad readability and language mixing."

To resolve this, the group utilized a short stage of SFT to avoid the "cold start" problem of RL. They gathered numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then gathered more SFT data utilizing rejection tasting, 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 design on a range of thinking, math, and coding standards and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several of the standards, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: DeepSeek-R1 Technical Report

Within a few days of its release, the that DeepSeek-R1 was ranked # 3 total in the arena and # 1 in coding and math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison blogged about his experiments with one of the DeepSeek distilled Llama models on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of thought used to help produce the action. [Given the prompt] "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 awful. But the process of getting there was such an interesting insight into how these new designs work.

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

DeepSeek is rapidly becoming a strong contractor of open models. Not just are these models fantastic entertainers, however their license allows use of their outputs for distillation, potentially pressing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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