GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning ability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 model on several benchmarks, including 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 variant of RL. The research team also performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched several versions of each; these designs surpass larger designs, including GPT-4, on math and coding benchmarks.

[DeepSeek-R1 is] the primary step towards enhancing language design thinking capabilities using pure support knowing (RL). Our objective is to check out the capacity of LLMs to establish reasoning capabilities with no monitored data, concentrating on their self-evolution through a pure RL process...DeepSeek-R1 ... master a vast array of tasks, of imaginative writing, general concern answering, editing, summarization, and more. Additionally, DeepSeek-R1 demonstrates outstanding performance on tasks requiring long-context understanding, significantly outperforming DeepSeek-V3 on long-context criteria.

To establish the design, DeepSeek started with DeepSeek-V3 as a base. They initially attempted fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a design called DeepSeek-R1-Zero, which they have actually likewise released. This model displays strong thinking performance, but" effective thinking habits, it faces numerous issues. For example, DeepSeek-R1-Zero battles with challenges like poor readability and language blending."

To address this, the group used a brief stage of SFT to prevent the "cold start" issue of RL. They collected a number of thousand examples of chain-of-thought thinking to utilize in SFT of DeepSeek-V3 before running RL. After the RL process converged, they then gathered more SFT data utilizing rejection sampling, 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 thinking, mathematics, and coding standards and compared it to other designs, consisting of Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed all of them on numerous of the criteria, including AIME 2024 and MATH-500.

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

Within a few days of its release, setiathome.berkeley.edu 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 discussed his try outs one of the DeepSeek distilled Llama models on his blog:

Each reaction starts with a ... pseudo-XML tag containing the chain of thought utilized to help generate the action. [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 awful. But the procedure of arriving was such an intriguing insight into how these brand-new models work.

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

DeepSeek is quickly emerging as a strong contractor of open models. Not only are these designs great entertainers, however their license allows use of their outputs for distillation, potentially pressing forward the cutting-edge for language models (and multimodal designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

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Anthony Alford

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