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

DeepSeek open-sourced DeepSeek-R1, hb9lc.org an LLM fine-tuned with reinforcement learning (RL) to improve reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on a number of standards, 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 design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several variations of each; these models exceed bigger designs, consisting of GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the very first step towards enhancing language design reasoning capabilities utilizing pure reinforcement learning (RL). Our goal is to check out the potential of LLMs to develop reasoning capabilities with no monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, including innovative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on tasks needing long-context understanding, considerably exceeding DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This model displays strong thinking efficiency, but" effective reasoning behaviors, it faces a number of concerns. For example, DeepSeek-R1-Zero battles with obstacles like bad readability and language blending."

To address this, the team used a short phase of SFT to prevent the "cold start" problem of RL. They collected numerous thousand systemcheck-wiki.de examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, disgaeawiki.info they then collected more SFT information using rejection tasting, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek examined their model on a range of reasoning, mathematics, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on numerous of the benchmarks, yewiki.org 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 general in the arena and # 1 in coding and math. It was likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison blogged about his try outs 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 help produce the reaction. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for setiathome.berkeley.edu 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 wrote about DeepSeek-R1:

DeepSeek is rapidly emerging as a strong builder of open designs. Not only are these entertainers, hb9lc.org however their license permits use of their outputs for distillation, possibly pressing forward the cutting-edge for language designs (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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