GitLab

DeepSeek open-sourced DeepSeek-R1, an LLM fine-tuned with support learning (RL) to improve thinking ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several standards, consisting of MATH-500 and SWE-bench.

DeepSeek-R1 is based upon DeepSeek-V3, a mixture of experts (MoE) model just recently open-sourced by DeepSeek. This base design is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a reasoning-oriented version of RL. The research group likewise performed knowledge distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched numerous variations of each; these designs exceed larger models, consisting of GPT-4, on mathematics and coding criteria.

[DeepSeek-R1 is] the primary step towards enhancing language design reasoning capabilities utilizing pure support knowing (RL). Our objective is to explore the capacity of LLMs to develop reasoning capabilities with no supervised data, focusing on their self-evolution through a pure RL process...DeepSeek-R1 ... excels in a vast array of tasks, higgledy-piggledy.xyz consisting of creative writing, basic question answering, modifying, summarization, and more. Additionally, DeepSeek-R1 shows impressive efficiency on tasks requiring long-context understanding, considerably exceeding DeepSeek-V3 on long-context criteria.

To establish the model, DeepSeek began with DeepSeek-V3 as a base. They initially tried fine-tuning it only with RL, and without any monitored fine-tuning (SFT), producing a model called DeepSeek-R1-Zero, which they have actually likewise released. This model exhibits strong thinking performance, but" effective thinking behaviors, it deals with a number of concerns. For instance, DeepSeek-R1-Zero fights with obstacles like bad readability and language mixing."

To resolve this, the group utilized a brief phase of SFT to prevent the "cold start" problem of RL. They gathered several thousand examples of chain-of-thought thinking to use in SFT of DeepSeek-V3 before running RL. After the RL procedure converged, they then collected more SFT information using rejection tasting, leading to a dataset of 800k samples. This dataset was utilized for further fine-tuning and to produce the distilled models from Llama and Qwen.

DeepSeek evaluated their model on a variety of reasoning, mathematics, and coding benchmarks and compared it to other models, including Claude-3.5- Sonnet, GPT-4o, and o1. DeepSeek-R1 surpassed 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 revealed that DeepSeek-R1 was ranked # 3 general in the arena and # 1 in coding and math. It was also connected for # 1 with o1 in "Hard Prompt with Style Control" classification.

Django structure co-creator Simon Willison blogged about his try outs one of the DeepSeek distilled Llama models on his blog site:

Each reaction begins with a ... pseudo-XML tag containing the chain of thought used to help produce the response. [Given the timely] "a joke about a pelican and a walrus who run a tea space together" ... It then believed for 20 paragraphs before outputting the joke! ... [T] he joke is awful. But the procedure of getting there was such an interesting insight into how these new models work.

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

DeepSeek is quickly emerging as a strong home builder of open models. Not just are these models fantastic entertainers, however their license permits use of their outputs for distillation, possibly pressing forward the cutting-edge for language designs (and designs) of all sizes.

The DeepSeek-R1 models are available on HuggingFace.

About the Author

Anthony Alford

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