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Penaakulan DeepSeek V3 dan R1
DeepSeek-V3 and DeepSeek-R1 are open-weight model releases with different technical contributions.
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Gambaran keseluruhan
V3 uses a mixture-of-experts architecture; R1 applies reinforcement learning and a multi-stage post-training pipeline to reasoning. Reported GPU-hour figures describe a specific V3 training run, not the full research cost or the cost to train R1.
Menyelam dalam
The DeepSeek-V3 report describes a 671B-parameter mixture-of-experts model with 37B activated per token. It reports 2.788 million H800 GPU-hours for the full V3 training run; this compute estimate is not the total cost of the research program and does not state the cost of training R1. DeepSeek-R1 builds on DeepSeek-V3-Base. Its paper describes a multi-stage pipeline: cold-start supervised fine-tuning, reasoning-focused reinforcement learning, rejection sampling and supervised fine-tuning, then a second RL stage. The paper evaluates verifiable tasks such as mathematics, coding competitions, and STEM. It also describes smaller distilled models based on Qwen and Llama families.
Pelaksanaan Dunia Sebenar
Inspecting the V3 technical report’s active-versus-total parameter description for its MoE architecture
Comparing R1’s reasoning evaluation on mathematics, coding, and STEM tasks
Studying the stages used to build R1 from a V3 base model
Using smaller R1-distilled models based on Qwen and Llama checkpoints for research
Masa Depan DeepSeek V3 dan R1 Penaakulan
DeepSeek’s reports contribute open-weight model artifacts and technical descriptions of MoE training and RL-based reasoning. Results should be tied to each model, checkpoint, benchmark, and training stage. Reported compute estimates are narrower than total research, infrastructure, and data costs.
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Take the DeepSeek V3 and R1 Reasoning quiz
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Soalan lazim
What is DeepSeek V3 and R1 Reasoning?
DeepSeek-V3 and DeepSeek-R1 are open-weight model releases with different technical contributions. V3 uses a mixture-of-experts architecture; R1 applies reinforcement learning and a multi-stage post-training pipeline to reasoning. Reported GPU-hour figures describe a specific V3 training run, not the full research cost or the cost to train R1.
What parameter counts did the DeepSeek-V3 technical report give?
The V3 report describes 671B total parameters and 37B active per token in its MoE architecture.
What was notable about DeepSeek-R1’s reasoning training and release?
The R1 paper describes reinforcement-learning-based reasoning and the project released model weights; the paper does not imply every capability is achieved by RL alone.
What compute figure did the DeepSeek-V3 report give for its full training run?
The V3 report lists 2.788M H800 GPU-hours for the full training run; this is not a full research-cost figure or an estimate for R1 training.
What training signal helped DeepSeek-R1 develop reasoning behaviors?
The R1 report describes RL using verifiable reward signals for reasoning tasks, alongside other stages in the full R1 pipeline.
Apakah satu teknik kecekapan yang dikaitkan dengan latihan DeepSeek-V3?
V3 memperkenalkan teknik seperti perhatian terpendam berbilang kepala dan skim pengimbangan beban tanpa kehilangan tambahan untuk melatih KPMnya dengan cekap.
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