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DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on numerous standards, including MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of experts (MoE) model just recently open-sourced by DeepSeek. This base model is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group also carried out knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released a number of versions of each; these models exceed larger designs, including GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards improving language design reasoning abilities using pure support knowing (RL). Our goal is to explore the capacity of LLMs to establish thinking abilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of jobs, including innovative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks requiring long-context understanding, significantly exceeding DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it just with RL, and without any supervised fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have likewise launched. This design displays strong reasoning efficiency, but" powerful reasoning habits, it deals with a number of concerns. For example, DeepSeek-R1-Zero has problem with obstacles like poor readability and language mixing."

To address this, the team utilized a short stage of SFT to prevent the "cold start" problem of RL. They collected numerous thousand examples of chain-of-thought reasoning to use 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 models from Llama and Qwen.

DeepSeek evaluated their model on a range of thinking, math, and and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on several of the benchmarks, 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 connected for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each action starts with a ... pseudo-XML tag containing the chain of idea used to assist create the action. [Given the prompt] "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 horrible. But the process of arriving was such an intriguing insight into how these new designs work.

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

DeepSeek is rapidly emerging as a strong contractor of open models. Not just are these models great entertainers, however their license permits usage of their outputs for distillation, possibly pressing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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