GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement knowing (RL) to enhance reasoning capability. DeepSeek-R1 attains results on par with OpenAI's o1 model on numerous benchmarks, consisting of MATH-500 and SWE-bench.

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

[DeepSeek-R1 is] the initial step toward improving language design thinking capabilities using pure reinforcement learning (RL). Our goal is to explore the capacity of LLMs to develop thinking abilities with no monitored information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, consisting of creative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional performance on tasks requiring long-context understanding, substantially outshining DeepSeek-V3 on long-context benchmarks.

To develop the model, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it just with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have likewise launched. This model shows strong reasoning efficiency, but" powerful thinking habits, it deals with several concerns. For example, DeepSeek-R1-Zero deals with challenges like poor readability and language blending."

To address this, the team used a brief phase of SFT to avoid 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 process converged, they then collected more SFT data using rejection tasting, leading to a dataset of 800k samples. This dataset was used for more fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek assessed their model on a range of reasoning, mathematics, and coding criteria and compared it to other designs, including Claude-3.5- Sonnet, setiathome.berkeley.edu GPT-4o, and o1. DeepSeek-R1 outperformed all of them on several of the benchmarks, 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 revealed that DeepSeek-R1 was ranked # 3 general 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 discussed his experiments with one of the DeepSeek distilled Llama designs on his blog site:

Each response begins with a ... tag containing the chain of idea used to help create the action. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is horrible. But the process of getting there was such an interesting insight into how these new designs work.

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

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

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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