Are there really hidden technical barriers to large models? Recently, Yao Shunyu, who was transferred from OpenAI to Tencent, once said, "There's no real secret to building a large model." At first, people thought it was just a joke, but after Tencent's Huan Yuan 4 (Hy4 preview) recently released its results, the value of this statement became evident.
Face with fierce competitors such as Zhipu, Yuedao Dark Face, DeepSeek, and Qwen, Tencent completed the restructuring of the Huan Yuan large language and multimodal department within four months and launched Huan Yuan 4 with 770B parameters and support for a million context. Upon closer examination of its technical configuration, it becomes clear that the so-called exclusive secrets of large models have been completely deconstructed by open-source mechanisms and the fast flow of top talent.
From the underlying structure, Huan Yuan 4 directly aligned with the engineering optimal solution previously verified by Zhipu GLM-5 (744B) at the medium-large scale level of 700B. Its main network has a hidden dimension of 6144, 78 layers, and 64 attention heads, which determine the unified choice of depth, width, and attention parallel structure in the Transformer network. For Tencent, which urgently needs to break through, standing on the consensus base validated by predecessors has avoided repeated mistakes.
In terms of core technology migration, Huan Yuan 4 cleverly integrates multiple open-source achievements in its attention module. Its main body draws on the sparse attention mechanism (DSA) open-sourced by DeepSeek in V3.2, using a lightweight indexer to select Top-K Tokens in ultra-long contexts, thus avoiding scanning all history. Subsequently, it also absorbed the cross-layer index reuse mechanism validated by Zhipu team in IndexCache, allowing adjacent Transformer layers to directly reuse existing index results, reducing the indexer computation by up to 75% and achieving end-to-end performance improvement. In addition, in residual optimization, Huan Yuan 4 abandoned complex mHC and adopted the identity hyperconnection (iHC) scheme proposed by Microsoft researcher Xie Tian in public discussions on social platforms.
Behind the technical relay is the deep movement of top talents. Bai Yushi, a graduate of Tsinghua University's Yao班, who once served as the lead architect of new model architecture and DSA for Zhipu GLM-5, officially joined the Tencent Huan Yuan team several months ago and appeared in the author list of the Huan Yuan long context sparse attention paper. It is precisely through these brilliant researchers that tacit engineering experience has physically migrated from one company to another.
Now, any technological advantage that can be expressed through papers, code, and experimental data has a rapidly shortening period of exclusivity. From top conference papers to open-source code, and then to community discussion posts, the dissemination path of cutting-edge architectures has been completely flattened, and the industry moat of large models is being rapidly filled by the open-source ecosystem and talent mobility.
