DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to improve reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of specialists (MoE) design just recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched several variations of each; these designs outshine bigger models, GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the initial step towards improving language model thinking capabilities using pure support learning (RL). Our objective is to explore the potential of LLMs to establish reasoning capabilities without any monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, including innovative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on jobs requiring long-context understanding, considerably outshining DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it only with RL, and with no supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have also released. This design shows strong thinking efficiency, however" powerful thinking habits, it faces a number of problems. For instance, DeepSeek-R1-Zero deals with difficulties like bad readability and language blending."

To address this, the team used a short stage of SFT to prevent the "cold start" problem of RL. They gathered several 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 sampling, 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 assessed their design on a range of thinking, mathematics, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined 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 couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison discussed his explores among the DeepSeek distilled Llama designs on his blog:

Each response begins with a ... pseudo-XML tag containing the chain of thought used to help create the action. [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 awful. But the process of getting there was such a fascinating insight into how these brand-new models work.

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

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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