GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with reinforcement learning (RL) to enhance reasoning ability. DeepSeek-R1 attains outcomes 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 mixture of professionals (MoE) design recently open-sourced by DeepSeek. This base design is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research study group likewise carried out understanding distillation from DeepSeek-R1 to open-source Qwen and Llama designs and released a number of variations of each; these models outperform bigger models, consisting of GPT-4, on math and coding standards.

[DeepSeek-R1 is] the first action towards improving language design reasoning capabilities using pure support learning (RL). Our goal is to check out the potential of LLMs to establish reasoning abilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide variety of tasks, consisting of creative writing, general concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows outstanding efficiency on jobs requiring long-context understanding, significantly outshining DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek started with DeepSeek-V3 as a base. They first attempted fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also released. This design shows strong thinking efficiency, however" powerful reasoning habits, it faces a number of concerns. For example, DeepSeek-R1-Zero fights with difficulties like bad readability and language blending."

To resolve this, the group used a brief stage of SFT to avoid the "cold start" issue of RL. They gathered numerous thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL process assembled, they then collected more SFT data using rejection tasting, leading to a dataset of 800k samples. This was utilized for further fine-tuning and hb9lc.org to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of reasoning, mathematics, and coding standards and compared it to other models, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 exceeded all of them on numerous of the benchmarks, including AIME 2024 and MATH-500.

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

Within a couple of days of its release, the LMArena revealed that DeepSeek-R1 was ranked # 3 overall 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 wrote about his experiments with among the DeepSeek distilled Llama models on his blog site:

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to assist generate the response. [Given the timely] "a joke about a pelican and a walrus who run a tea room together" ... It then thought for 20 paragraphs before outputting the joke! ... [T] he joke is dreadful. But the process of arriving was such an interesting insight into how these new models work.

Andrew Ng's newsletter The Batch discussed DeepSeek-R1:

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

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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