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deepseek-v4 reinforcement-learning open-source-ai hybrid-architectures

DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles

lmsys.org

April 25, 2026

17 min read

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48/100

Summary

DeepSeek-V4 is now supported for both inference and reinforcement learning (RL) training from Day 0. SGLang and Miles provide the first open-source stack designed for DeepSeek-V4’s hybrid sparse-attention architecture and manifold-constrained hyper-connections, utilizing FP4 expert weights.

Key Takeaways

DeepSeek-V4 features Day-0 support for both inference and reinforcement learning training, utilizing SGLang and Miles as the first open-source stack for its launch.
The model incorporates hybrid sparse-attention, manifold-constrained hyper-connections (mHC), and FP4 expert weights for enhanced performance and efficiency.
DeepSeek-V4 supports a 1M-token context window by combining sliding window attention with two compression mechanisms, allowing for efficient processing of large inputs.
The ShadowRadix caching mechanism enables coherent management of multiple heterogeneous key-value pools, optimizing the handling of hybrid attention across layers.

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