GitLab

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

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

[DeepSeek-R1 is] the first action towards enhancing language design reasoning abilities using pure support learning (RL). Our goal is to check out the potential of LLMs to develop reasoning abilities with no monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of tasks, including creative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on jobs requiring long-context understanding, considerably outperforming DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially attempted it just with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also launched. This model exhibits strong thinking efficiency, however" effective thinking behaviors, it deals with a number of problems. For example, DeepSeek-R1-Zero fights with challenges 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 several thousand examples of chain-of-thought reasoning to use in SFT of DeepSeek-V3 before running RL. After the RL procedure assembled, they then gathered more SFT information utilizing rejection sampling, setiathome.berkeley.edu leading to a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek assessed their model on a range of thinking, math, and coding benchmarks and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several of the standards, consisting of AIME 2024 and MATH-500.

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

Within a few 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" category.

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

Each reaction starts with a ... pseudo-XML tag containing the chain of thought utilized to help produce the reaction. [Given the prompt] "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 dreadful. 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 emerging as a strong contractor of open models. Not only are these models great entertainers, however their license allows use of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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