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

DeepSeek-R1 is based upon DeepSeek-V3, a mix of professionals (MoE) model recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented variant of RL. The research group also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and launched a number of variations of each; these designs exceed bigger models, including GPT-4, on mathematics and coding standards.

[DeepSeek-R1 is] the primary step toward improving language model reasoning abilities utilizing pure reinforcement learning (RL). Our objective is to check out the potential of LLMs to develop thinking abilities with no monitored information, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... master a wide range of jobs, bytes-the-dust.com consisting of creative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates impressive performance on tasks needing long-context understanding, pediascape.science considerably surpassing DeepSeek-V3 on long-context benchmarks.

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

To resolve this, the group used a short stage of SFT to prevent the "cold start" problem of RL. They collected several thousand examples of chain-of-thought reasoning to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then collected more SFT data utilizing rejection sampling, leading to a dataset of 800k samples. This dataset was utilized for more fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek examined their design on a variety of reasoning, math, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 outshined all of them on numerous of the criteria, 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 likewise tied for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison wrote about his experiments with one of the DeepSeek distilled Llama designs on his blog:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought used to assist generate the response. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then thought for setiathome.berkeley.edu 20 paragraphs before outputting the joke! ... [T] he joke is terrible. But the process of arriving was such an intriguing insight into how these new models work.

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

DeepSeek is quickly emerging as a strong home builder of open models. Not just are these models great entertainers, it-viking.ch however their license permits use 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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