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 enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI’s o1 model on numerous criteria, including MATH-500 and larsaluarna.se SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team also carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of versions of each; these models surpass larger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the primary step toward enhancing language design thinking abilities using pure reinforcement learning (RL). Our objective is to check out the capacity of LLMs to establish reasoning capabilities with no supervised information, focusing on their self-evolution through a pure RL process…DeepSeek-R1 … excels in a wide variety of tasks, consisting of creative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows impressive performance on jobs requiring long-context understanding, significantly surpassing DeepSeek-V3 on long-context benchmarks.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This model displays strong reasoning efficiency, however” effective thinking habits, it faces numerous problems. For instance, DeepSeek-R1-Zero has a hard time with challenges like poor readability and language blending.”

To resolve this, the group utilized a short phase of SFT to prevent the “cold start” issue of RL. They gathered a number of thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information utilizing rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for wiki.snooze-hotelsoftware.de additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their design on a range of thinking, mathematics, and coding standards and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the criteria, including 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 overall in the arena and # 1 in coding and math. It was likewise connected for # 1 with o1 in “Hard Prompt with Style Control” classification.

Django framework co-creator Simon Willison blogged about his experiments with among the DeepSeek distilled Llama designs on his blog site:

Each reaction starts with a … pseudo-XML tag containing the chain of thought utilized to help produce the response. [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 dreadful. But the procedure of getting there was such an intriguing insight into how these new designs work.

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

DeepSeek is becoming a strong contractor of open designs. Not only are these models fantastic entertainers, but their license permits usage of their outputs for distillation, pipewiki.org potentially pressing forward the cutting-edge for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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