DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on several criteria, including MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (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 study group likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several variations of each; these models outperform bigger designs, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the very first step toward improving language design thinking capabilities using pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to establish reasoning capabilities without any supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, including creative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on jobs needing long-context understanding, substantially surpassing DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, bytes-the-dust.com and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also launched. This model displays strong thinking performance, but" powerful thinking habits, it faces several problems. For example, DeepSeek-R1-Zero fights with difficulties like poor readability and language blending."

To address this, the group used a short phase of SFT to prevent the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought reasoning 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 utilized for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a variety of thinking, math, and coding standards and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outperformed all of them on several of the benchmarks, including AIME 2024 and MATH-500.

DeepSeek-R1 Performance. Image Source: wiki.myamens.com DeepSeek-R1 Technical Report

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

Django structure co-creator Simon Willison wrote about his try outs one of the DeepSeek distilled Llama models on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of idea used to help produce the response. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is terrible. But the procedure of arriving was such a fascinating insight into how these new models work.

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

DeepSeek is quickly becoming a strong builder of open models. Not just are these models great entertainers, but their license permits use of their outputs for distillation, possibly pushing forward the cutting-edge for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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