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| <h1 class="title is-1 publication-title">DeepSeek Papers</h1> | |
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| Advancing Open-Source Language Models | |
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| <!-- Abstract. --> | |
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| <h2 class="title is-3">DeepSeek Research Contributions</h2> | |
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| <p> | |
| Below is a list of significant papers by DeepSeek detailing advancements in large language models (LLMs), | |
| ordered by release date from most recent to oldest. Each paper includes a brief description and highlights | |
| upcoming deep dives. | |
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| <a href="#">DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <strong>Release Date:</strong> January 20, 2025 | |
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| The R1 model enhances reasoning capabilities through large-scale reinforcement learning, competing | |
| directly with leading models like OpenAI's o1. | |
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| <a href="#">DeepSeek-V3 Technical Report</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <strong>Release Date:</strong> December 2024 | |
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| This report discusses the scaling of sparse MoE networks to 671 billion parameters, utilizing mixed | |
| precision training and HPC co-design strategies. | |
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| <a href="#">DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <strong>Release Date:</strong> May 2024 | |
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| This paper introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing | |
| training costs by 42%. | |
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| <a href="#">DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <strong>Release Date:</strong> April 2024 | |
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| This paper presents methods to improve mathematical reasoning in LLMs, introducing the Group | |
| Relative Policy Optimization (GRPO) algorithm. | |
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| <a href="#">DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <strong>Release Date:</strong> November 29, 2023 | |
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| This foundational paper explores scaling laws and the trade-offs between data and model size, | |
| establishing the groundwork for subsequent models. | |
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| <a href="#">DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| Focuses on enhancing theorem proving capabilities in language models using synthetic data for training. | |
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| <a href="#">DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| <div class="publication-description"> | |
| This paper details advancements in code-related tasks with an emphasis on open-source methodologies, | |
| improving upon earlier coding models. | |
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| <a href="#">DeepSeekMoE</a> | |
| <span class="tag is-info is-light">[Deep Dive Coming Soon]</span> | |
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| Discusses the integration and benefits of the Mixture-of-Experts approach within the DeepSeek framework. | |
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