DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve thinking capability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of experts (MoE) model recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released a number of variations of each; these models surpass bigger models, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step towards improving language model reasoning capabilities using pure support learning (RL). Our goal is to check out the potential of LLMs to develop thinking abilities without any monitored data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a large range of tasks, including imaginative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on tasks requiring long-context understanding, considerably exceeding 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 with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, wiki.asexuality.org which they have actually also launched. This model exhibits strong reasoning efficiency, however" powerful thinking behaviors, it deals with several problems. For example, DeepSeek-R1-Zero has a hard time with challenges like poor readability and language mixing."

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

DeepSeek examined their model on a variety of thinking, math, wiki.lafabriquedelalogistique.fr 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 benchmarks, consisting of 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 general in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

Django structure co-creator Simon Willison discussed his try outs among the DeepSeek distilled Llama designs on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of thought utilized to assist create the reaction. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. But the process of getting there was such an interesting insight into how these new models work.

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

DeepSeek is quickly becoming a strong contractor of open designs. Not just are these models fantastic entertainers, however their license allows use of their outputs for distillation, potentially pressing forward the state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

About the Author

Anthony Alford

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