GitLab

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

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (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 study group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and and released several variations of each; these models exceed bigger designs, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the initial step towards enhancing language design thinking capabilities using pure support knowing (RL). Our goal is to check out the potential of LLMs to establish thinking capabilities without any supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, including creative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on jobs requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually likewise launched. This design shows strong reasoning efficiency, however" effective thinking habits, it deals with several problems. For example, DeepSeek-R1-Zero struggles with challenges like bad readability and language mixing."

To resolve this, systemcheck-wiki.de the group used a brief stage of SFT to avoid the "cold start" issue of RL. They collected several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model on a range of thinking, math, and coding criteria 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 standards, 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 tied for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each reaction begins with a ... pseudo-XML tag containing the chain of idea used to assist produce the reaction. [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 dreadful. But the process of arriving was such a fascinating insight into how these new designs work.

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

DeepSeek is rapidly becoming a strong contractor of open models. Not only are these designs great entertainers, but their license allows usage of their outputs for distillation, possibly pushing forward the cutting-edge 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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