GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to enhance thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous criteria, including 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 using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research study group also carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released numerous variations of each; these models outshine larger models, including GPT-4, on math and coding standards.

[DeepSeek-R1 is] the very first step toward improving language design reasoning abilities using pure support knowing (RL). Our goal is to check out the potential of LLMs to establish thinking abilities with no supervised information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, consisting of innovative writing, basic question answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on jobs needing long-context understanding, considerably surpassing DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried 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 model shows strong thinking performance, however" powerful thinking habits, it faces numerous problems. For instance, DeepSeek-R1-Zero fights with difficulties like bad readability and language mixing."

To address this, the team utilized a brief phase of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information using rejection sampling, resulting in a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their model on a range of thinking, mathematics, and coding standards and compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on several of the criteria, including AIME 2024 and MATH-500.

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

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

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

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to assist produce the response. [Given the prompt] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for surgiteams.com 20 paragraphs before outputting the joke! ... [T] he joke is terrible. But the procedure of getting there was such a fascinating insight into how these brand-new designs work.

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

DeepSeek is quickly becoming a strong builder of open models. Not only are these models terrific entertainers, however their license permits usage of their outputs for distillation, potentially pushing forward the cutting-edge for language models (and multimodal models) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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