News
All the latest updates and announcements.
| Aug 15, 2024 | One model DeepSeek-Prover-V1.5 has been released. This model enhances theorem proving in Lean 4 with state-of-the-art performance, including a 63.5% success rate on miniF2F. Available for research and commercial use. |
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| May 23, 2024 | One model DeepSeek-Prover achieves new state-of-the-art results in theorem proving, leveraging large-scale synthetic data to outperform GPT-4 on benchmarks like miniF2F and FIMO. Available for research and commercial use. |
| May 7, 2024 | One model DeepSeek-V2, a 236B Mixture-of-Experts language model, has been released. It offers stronger performance with 42.5% lower training costs and 93.3% reduced KV cache. Available for research and commercial use. |
| Mar 8, 2024 | One model DeepSeek-VL, a state-of-the-art Vision-Language model designed for real-world applications, is now available. Supports multimodal tasks like logical diagrams, scientific literature, and more. Released for research and commercial use. |
| Feb 24, 2024 | One paper Grasp Multiple Objects With One Hand accepted at RA-L 2024. Presented in the IROS 2024 as an Oral Presentation. |
| Jan 5, 2024 | One model DeepSeek-LLM, a 67B parameter language model, outperforms LLaMA2 70B in key tasks. Available for research and commercial use. |
| Nov 10, 2023 | One paper TorchOpt: An Efficient Library for Differentiable Optimization accepted at JMLR 2023. |
| May 19, 2023 | One open source project TorchOpt accepted as a PyTorch Ecosystem project. Check out the blog post! |
| Oct 21, 2022 | One paper TorchOpt: An Efficient Library for Differentiable Optimization accepted at NeurIPS 2022 Workshop OPT. |
| Sep 27, 2022 | Two papers A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning and EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine accepted at NeurIPS 2022. |
| Sep 29, 2021 | One paper Neural Auto-Curricula in Two-Player Zero-Sum Games accepted at NeurIPS 2021. |
| Dec 18, 2020 | One paper Learning Correlated Communication Topology in Multi-Agent Reinforcement learning accepted at AAMAS 2021 as an Oral Presentation. |