GitLab

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

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research study group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of versions of each; these designs exceed larger models, including GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the primary step towards enhancing language model thinking abilities utilizing pure support learning (RL). Our goal is to explore the potential of LLMs to develop reasoning capabilities without any monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, including imaginative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional performance on jobs needing long-context understanding, substantially outperforming DeepSeek-V3 on long-context benchmarks.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This design exhibits strong thinking efficiency, however" effective reasoning behaviors, it faces numerous issues. For example, DeepSeek-R1-Zero deals with difficulties like poor readability and language mixing."

To address this, the group utilized a brief stage of SFT to avoid the "cold start" issue 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 process converged, they then collected more SFT information using rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their design on a range of thinking, mathematics, and coding criteria and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the standards, consisting of AIME 2024 and MATH-500.

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

Within a few days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and mathematics. It was likewise connected for archmageriseswiki.com # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each reaction starts with a ... pseudo-XML tag containing the chain of idea utilized to assist create the action. [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 terrible. But the procedure of getting there was such a fascinating insight into how these new models work.

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

DeepSeek is quickly emerging as a strong contractor of open models. Not only are these designs terrific entertainers, but their license allows usage of their outputs for distillation, potentially pushing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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