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Imbue Reasoning Agents
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DeepSeek V3 uye R1 Kukurukurirana
DeepSeek-V3 and DeepSeek-R1 are open-weight model releases with different technical contributions.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
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.
Kudzika Kwakadzika
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.
Kuitwa muNyika yeChokwadi
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
Ramangwana reDeepSeek V3 uye R1 Kukurukurirana
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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Mibvunzo inowanzo bvunzwa
Chii chinonzi DeepSeek V3 uye R1 Kukurukurirana?
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.
Ndeipi parameter inoverengera iyo DeepSeek-V3 technical report yakapa?
Chirevo cheV3 chinotsanangura 671B yakazara ma paramita uye 37B inoshanda pane tokeni mune yayo MoE dhizaini.
Chii chaizivikanwa nezve DeepSeek-R1's kufunga kudzidziswa uye kusunungurwa?
Bepa reR1 rinotsanangura kusimbisa-kudzidza-kwakavakirwa kufunga uye chirongwa chakaburitswa maremu emuenzaniso; bepa haritauri kuti kugona kwese kunowanikwa neRL chete.
Ndeipi nhamba yekombuta iyo DeepSeek-V3 rondedzero yakapa kune yayo yakazara kudzidziswa kumhanya?
Chirevo cheV3 chinonyora 2.788M H800 GPU-maawa ekudzidzira kwakazara kumhanya; iyi haisi nhamba yakazara yekutsvagisa-mutengo kana fungidziro yeR1 kudzidziswa.
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.
Ndeipi imwe nzira inoshanda yakabatana nekudzidziswa kweDeepSeek-V3?
V3 yakaunza hunyanzvi senge-multi-head latent kutarisisa uye yekubatsira-kurasikirwa-yemahara mutoro-kuenzanisa chirongwa kudzidzisa MoE yayo zvakanaka.
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