DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning 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 experts (MoE) design recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group also carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and released numerous variations of each; these designs exceed larger models, consisting of GPT-4, on math and coding criteria.

[DeepSeek-R1 is] the first action towards enhancing language design reasoning abilities utilizing pure support knowing (RL). Our goal is to explore the potential of LLMs to develop thinking capabilities without any monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a large range of jobs, consisting of innovative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding efficiency on jobs requiring long-context understanding, substantially exceeding DeepSeek-V3 on long-context standards.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it just with RL, and with no supervised fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This model displays strong reasoning performance, but" powerful thinking behaviors, it faces a number of concerns. For example, DeepSeek-R1-Zero has a hard time with challenges like poor readability and language blending."

To resolve this, the group utilized a short phase of SFT to avoid the "cold start" problem of RL. They gathered several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information utilizing rejection tasting, leading to a dataset of 800k samples. This dataset was used for further fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their model 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 several of the criteria, consisting of AIME 2024 and MATH-500.

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

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

Django framework co-creator Simon Willison blogged about his experiments with one of the DeepSeek distilled Llama models on his blog:

Each action begins with a ... pseudo-XML tag containing the chain of idea used to assist generate the action. [Given the prompt] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is dreadful. But the process of getting there was such a fascinating insight into how these brand-new models work.

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

DeepSeek is quickly as a strong contractor of open models. Not just are these models fantastic entertainers, surgiteams.com but their license permits usage of their outputs for distillation, potentially pressing 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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