GitLab

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 design on several standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of professionals (MoE) design just recently open-sourced by DeepSeek. This base model is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research study team likewise carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous variations of each; these designs outperform larger designs, consisting of GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step towards improving language design reasoning abilities using pure support knowing (RL). Our objective is to explore the capacity of LLMs to develop thinking capabilities without any supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a broad variety of jobs, consisting of creative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive efficiency on tasks requiring long-context understanding, substantially exceeding DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise launched. This model displays strong thinking performance, but" effective thinking habits, it deals with several problems. For circumstances, DeepSeek-R1-Zero struggles with obstacles like poor readability and language blending."

To resolve this, the team utilized a brief phase of SFT to avoid the "cold start" problem of RL. They collected a number of thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT data using rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

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

DeepSeek-R1 Performance. Image Source: setiathome.berkeley.edu DeepSeek-R1 Technical Report

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

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

Each action begins with a ... pseudo-XML tag containing the chain of idea used to help produce the reaction. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for wavedream.wiki 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the process of getting there was such an intriguing insight into how these new designs work.

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

DeepSeek is quickly becoming a strong contractor of open designs. Not just are these models fantastic entertainers, but their license permits usage 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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