GitLab

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

DeepSeek-R1 is based on DeepSeek-V3, wavedream.wiki a mix of specialists (MoE) model recently open-sourced by DeepSeek. This is fine-tuned using Group Relative Policy Optimization (GRPO), a reasoning-oriented variation of RL. The research team likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama designs and wakewiki.de released several variations of each; these designs outshine bigger designs, including GPT-4, pipewiki.org on math and coding standards.

[DeepSeek-R1 is] the first step towards improving language model thinking abilities using pure reinforcement knowing (RL). Our objective is to check out the potential of LLMs to develop thinking abilities with no supervised data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a wide variety of tasks, including innovative writing, basic concern answering, modifying, summarization, and more. Additionally, DeepSeek-R1 demonstrates exceptional efficiency on tasks requiring long-context understanding, significantly outshining DeepSeek-V3 on long-context standards.

To develop the model, DeepSeek began with DeepSeek-V3 as a base. They first tried fine-tuning it only with RL, and higgledy-piggledy.xyz with no monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have also launched. This model shows strong thinking efficiency, however" effective reasoning behaviors, it faces numerous issues. For example, DeepSeek-R1-Zero fights with obstacles like poor readability and language blending."

To resolve this, the group utilized a short phase of SFT to prevent 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 collected more SFT information utilizing rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for additional fine-tuning and to produce the distilled designs from Llama and Qwen.

DeepSeek evaluated their design on a range of reasoning, mathematics, and coding benchmarks and forum.batman.gainedge.org compared it to other designs, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed 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 couple of days of its release, the LMArena announced that DeepSeek-R1 was ranked # 3 overall in the arena and # 1 in coding and math. It was also tied for # 1 with o1 in "Hard Prompt with Style Control" category.

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

Each action starts with a ... pseudo-XML tag containing the chain of thought utilized to help generate the action. [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 terrible. But the process of getting there was such an interesting insight into how these brand-new designs work.

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

DeepSeek is rapidly becoming a strong contractor of open models. Not only are these designs fantastic entertainers, but their license permits usage of their outputs for distillation, possibly pressing forward the cutting-edge for language designs (and oeclub.org multimodal models) of all sizes.

The DeepSeek-R1 designs are available on HuggingFace.

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Anthony Alford

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