GitLab

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 design on a number of criteria, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based on DeepSeek-V3, a mix of professionals (MoE) design 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 likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several variations of each; these designs outperform larger designs, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the initial step toward enhancing language design reasoning abilities using pure reinforcement learning (RL). Our objective is to explore the potential of LLMs to establish reasoning capabilities with no supervised information, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of jobs, consisting of imaginative writing, general question answering, editing, summarization, and more. Additionally, DeepSeek-R1 shows exceptional efficiency on tasks requiring long-context understanding, substantially outshining DeepSeek-V3 on long-context criteria.

To develop the design, DeepSeek began with DeepSeek-V3 as a base. They first tried it only with RL, and with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually also launched. This design displays strong thinking performance, but" powerful reasoning behaviors, it faces a number of issues. For example, DeepSeek-R1-Zero has a hard time with difficulties like poor readability and language mixing."

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

DeepSeek examined their model on a variety of reasoning, mathematics, and coding standards 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 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 announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and mathematics. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django framework co-creator Simon Willison composed about his explores among the DeepSeek distilled Llama models on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of idea used to help produce the response. [Given the prompt] "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 brand-new models work.

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

DeepSeek is rapidly emerging as a strong builder of open designs. Not only are these designs excellent entertainers, however their license allows usage of their outputs for distillation, possibly pressing forward the state of the art for language designs (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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