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 knowing (RL) to enhance thinking capability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) design 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 team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and gratisafhalen.be Llama designs and released a number of variations of each; these designs exceed larger designs, consisting of GPT-4, on mathematics and coding benchmarks.

[DeepSeek-R1 is] the initial step toward enhancing language model thinking capabilities using pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to develop thinking abilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, consisting of imaginative writing, basic question answering, editing, summarization, trademarketclassifieds.com and more. Additionally, DeepSeek-R1 shows impressive performance on tasks requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context benchmarks.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise launched. This design shows strong reasoning performance, but" effective reasoning habits, it faces a number of issues. For example, DeepSeek-R1-Zero deals with difficulties like bad readability and language mixing."

To resolve this, the group utilized a brief stage of SFT to prevent the "cold start" issue of RL. They gathered several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then collected more SFT information using rejection tasting, resulting in a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of reasoning, mathematics, and coding criteria and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on several of the criteria, including AIME 2024 and MATH-500.

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

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

Django framework co-creator pipewiki.org Simon Willison blogged about his explores one of the DeepSeek distilled Llama designs on his blog site:

Each action begins 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 awful. But the process of arriving was such an interesting insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong contractor of open designs. Not just are these models excellent entertainers, however their license permits usage of their outputs for distillation, possibly pushing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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