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| <h1 class="title is-1 publication-title">DeepSeek Research Papers</h1> | |
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| <span class="author-block">DeepSeek Research Team</span> | |
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| Advancing AI through Open Research and Innovation | |
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| <h2 class="title is-3 has-text-centered">Publications</h2> | |
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| <h3 class="title is-4">1. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism</h3> | |
| <p>Scaling open-source language models with a focus on longtermism.</p> | |
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| <a href="https://huggingface.co/papers/2401.02954" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jan 6, 2024}</span> | |
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| <h3 class="title is-4">2. DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models</h3> | |
| <p>Exploring expert specialization in Mixture-of-Experts language models.</p> | |
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| <a href="https://arxiv.org/abs/2401.06066" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jan 11, 2024}</span> | |
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| <h3 class="title is-4">3. DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence</h3> | |
| <p>Investigating the intersection of large language models and programming.</p> | |
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| <a href="https://arxiv.org/abs/2401.14196" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jan 25, 2024}</span> | |
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| <!-- Paper 4 --> | |
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| <h3 class="title is-4">4. DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models</h3> | |
| <p>Advancing mathematical reasoning capabilities in open language models.</p> | |
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| <a href="https://arxiv.org/abs/2402.03300" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Feb 6, 2024}</span> | |
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| <!-- Paper 5 --> | |
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| <h3 class="title is-4">5. DeepSeek-VL: Towards Real-World Vision-Language Understanding</h3> | |
| <p>Focusing on real-world vision-language understanding.</p> | |
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| <a href="https://arxiv.org/abs/2403.05525" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Mar 9, 2024}</span> | |
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| <!-- Paper 6 --> | |
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| <h3 class="title is-4">6. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</h3> | |
| <p>Developing a strong, economical, and efficient Mixture-of-Experts language model.</p> | |
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| <a href="https://arxiv.org/abs/2405.04434" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{May 7, 2024}</span> | |
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| <!-- Paper 7 --> | |
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| <h3 class="title is-4">7. DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data</h3> | |
| <p>Using large-scale synthetic data to advance theorem proving in LLMs.</p> | |
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| <a href="https://arxiv.org/abs/2405.14333" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{May 23, 2024}</span> | |
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| <!-- Paper 8 --> | |
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| <h3 class="title is-4">8. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence</h3> | |
| <p>Aiming to surpass closed-source models in code intelligence.</p> | |
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| <a href="https://arxiv.org/abs/2406.11931" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jun 17, 2024}</span> | |
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| <!-- Paper 9 --> | |
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| <h3 class="title is-4">9. Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models</h3> | |
| <p>Fine-tuning sparse architectural large language models with expert specialization.</p> | |
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| <a href="https://arxiv.org/abs/2407.01906" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jul 2, 2024}</span> | |
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| <!-- Paper 10 --> | |
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| <h3 class="title is-4">10. DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search</h3> | |
| <p>Utilizing proof assistant feedback for reinforcement learning and Monte-Carlo Tree Search.</p> | |
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| <a href="https://arxiv.org/abs/2408.08152" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Aug 15, 2024}</span> | |
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| <!-- Paper 11 --> | |
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| <h3 class="title is-4">11. Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation</h3> | |
| <p>Decoupling visual encoding for unified multimodal understanding and generation.</p> | |
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| <a href="https://arxiv.org/abs/2410.13848" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Oct 17, 2024}</span> | |
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| <!-- Paper 12 --> | |
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| <h3 class="title is-4">12. JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation</h3> | |
| <p>Harmonizing autoregression and rectified flow for unified multimodal understanding and generation.</p> | |
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| <a href="https://arxiv.org/abs/2411.07975" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Nov 12, 2024}</span> | |
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| <!-- Paper 13 --> | |
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| <h3 class="title is-4">13. DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding</h3> | |
| <p>Mixture-of-Experts Vision-Language Models for advanced multimodal understanding.</p> | |
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| <a href="https://arxiv.org/abs/2412.10302" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Dec 13, 2024}</span> | |
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| <!-- Paper 14 --> | |
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| <h3 class="title is-4">14. DeepSeek-V3 Technical Report</h3> | |
| <p>Technical report for DeepSeek-V3.</p> | |
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| <a href="https://arxiv.org/abs/2412.19437" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Dec 27, 2024}</span> | |
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| <!-- Paper 15 --> | |
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| <h3 class="title is-4">15. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning</h3> | |
| <p>Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.</p> | |
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| <a href="https://arxiv.org/abs/2501.12948" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jan 27, 2025}</span> | |
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| <!-- Paper 16 --> | |
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| <h3 class="title is-4">16. Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling</h3> | |
| <p>Unified Multimodal Understanding and Generation with Data and Model Scaling.</p> | |
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| <a href="https://arxiv.org/abs/2501.17811" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Jan 31, 2025}</span> | |
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| <!-- Paper 17 --> | |
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| <h3 class="title is-4">17. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention</h3> | |
| <p>Hardware-Aligned and Natively Trainable Sparse Attention.</p> | |
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| <a href="https://arxiv.org/abs/2502.11089" target="_blank" | |
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| <span class="is-size-6 has-text-grey">{Feb 16, 2025}</span> | |
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