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


DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on a number of standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released several versions of each; these models outperform bigger designs, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the very first action toward enhancing language design reasoning abilities utilizing pure support learning (RL). Our goal is to explore the capacity of LLMs to establish reasoning capabilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of jobs, consisting of imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional performance on tasks needing long-context understanding, significantly exceeding DeepSeek-V3 on long-context benchmarks.

To develop the design, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also launched. This model displays strong reasoning efficiency, but" effective reasoning habits, it deals with a number of concerns. For instance, DeepSeek-R1-Zero has problem with obstacles like bad readability and language mixing."

To resolve this, the group utilized a brief phase of SFT to avoid the "cold start" issue 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 process assembled, they then gathered more SFT information using rejection tasting, 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 design on a range of thinking, mathematics, and coding benchmarks 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, consisting of AIME 2024 and MATH-500.

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

Within a few days of its release, the that DeepSeek-R1 was ranked # 3 general 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 structure co-creator Simon Willison blogged about his try outs among the DeepSeek distilled Llama models on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of idea utilized 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 terrible. But the procedure of arriving was such an interesting insight into how these new designs work.

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

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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