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

DeepSeek-R1 is based on DeepSeek-V3, a mixture of professionals (MoE) design 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 study group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and released numerous variations of each; these designs exceed bigger designs, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the very first action towards enhancing language design reasoning capabilities using pure support knowing (RL). Our goal is to check out the capacity of LLMs to develop reasoning abilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a of jobs, consisting of creative writing, basic concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on jobs requiring long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first tried fine-tuning it just with RL, and with no monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually also released. This design displays strong thinking performance, but" effective reasoning habits, it faces several concerns. For instance, DeepSeek-R1-Zero fights with obstacles 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 collected several thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT data using rejection sampling, leading to a dataset of 800k samples. This dataset was used for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model on a range of thinking, mathematics, and coding criteria and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded 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 few days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and math. It was also connected 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:

Each action starts with a ... pseudo-XML tag containing the chain of idea used to assist generate the response. [Given the timely] "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 terrible. But the process of getting there was such an intriguing insight into how these brand-new models work.

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

DeepSeek is rapidly emerging as a strong home builder of open models. Not only are these models great entertainers, but their license allows usage of their outputs for archmageriseswiki.com distillation, possibly pushing forward the state of the art for language models (and multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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