DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI’s O1 Model

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve thinking ability. DeepSeek-R1 attains outcomes on par with OpenAI’s o1 model on numerous benchmarks, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) model just 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 performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs surpass larger models, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the primary step towards enhancing language design reasoning capabilities using pure reinforcement knowing (RL). Our objective is to explore the capacity of LLMs to establish thinking capabilities with no supervised data, concentrating on their self-evolution through a pure RL process…DeepSeek-R1 … master a large range of tasks, including innovative writing, general question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on jobs requiring long-context understanding, significantly exceeding DeepSeek-V3 on long-context benchmarks.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise launched. This design shows strong reasoning efficiency, but” effective reasoning behaviors, it deals with several issues. For instance, DeepSeek-R1-Zero fights with obstacles like poor readability and language mixing.”

To address this, gratisafhalen.be the group utilized a brief stage of SFT to prevent 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 process converged, they then collected more SFT information utilizing rejection tasting, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and bio.rogstecnologia.com.br to produce the from Llama and higgledy-piggledy.xyz Qwen.

DeepSeek examined their design on a variety of thinking, mathematics, and coding benchmarks and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the criteria, 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 revealed that DeepSeek-R1 was ranked # 3 total in the arena and # 1 in coding and mathematics. It was also tied 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 site:

Each action begins with a … pseudo-XML tag containing the chain of idea utilized to help generate 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 process of arriving was such an intriguing insight into how these new designs work.

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

DeepSeek is rapidly becoming a strong home builder of open models. Not only are these models terrific entertainers, but their license permits usage of their outputs for distillation, potentially pushing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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