GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on a number of criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several variations of each; these models outshine bigger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step toward enhancing language model thinking capabilities using pure support knowing (RL). Our goal is to explore the potential of LLMs to develop thinking abilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, including creative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on jobs needing long-context understanding, significantly surpassing 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 with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise released. This design exhibits strong thinking efficiency, but" powerful reasoning behaviors, it faces several issues. For instance, DeepSeek-R1-Zero has a hard time with challenges like poor readability and language blending."

To address this, the group utilized a short phase of SFT to avoid the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a variety of thinking, mathematics, and coding standards and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several 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, the LMArena announced that DeepSeek-R1 was ranked # 3 total 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 wrote about his experiments with among the DeepSeek distilled Llama models on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of thought utilized to assist produce the response. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is dreadful. But the procedure of getting there was such a fascinating insight into how these brand-new designs work.

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

DeepSeek is rapidly becoming a strong contractor setiathome.berkeley.edu of open designs. Not only are these designs fantastic entertainers, however their license allows use of their outputs for distillation, possibly pushing forward the state of the art 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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