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WireUnwired Research • Key Insights
- Alibaba Cloud’s Qwen team secures NeurIPS 2025 Best Paper for Gated Attention mechanism, stabilizing LLM training at scale.
- Deploys immediately in Qwen3-Next, slashing costs and boosting efficiency without accuracy loss; NeurIPS judges predict global adoption.
- Over 30 experiments on 15B MoE and 1.7B dense models trained on 3.5T tokens validate the tweak’s robustness.
Alibaba Cloud’s Qwen team just won a NeurIPS 2025 Best Paper award. Their paper, “Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free,” redefines LLM efficiency. Selected from 21,575 submissions, it stands out as industrial-scale research shared openly. While US labs hoard secrets, Alibaba publishes.
The Gated Attention Fix
Attention mechanisms power LLMs. They let models focus on relevant tokens. But spikes destabilize training. Gated Attention adds a simple sigmoid gate after Scaled Dot-Product Attention. This per-head controller multiplies outputs. It filters noise. It kills attention sinks—where models fixate on early tokens uselessly.
The gate preserves sparsity in key sequence areas. Result? Smoother scaling. Bigger learning rates. No crashes. Alibaba tested 30+ variants. That includes 15B-parameter MoE models and 1.7B dense ones. All on a 3.5-trillion-token dataset. Every architecture improved.
From Paper to Production
Qwen3-Next deploys it now. Combines Gated DeltaNet with Gated Attention. In-context learning surges. Compute efficiency jumps. Training and inference costs drop sharply. Accuracy holds firm. Code and models hit open platforms. Community can grab them today.
NeurIPS judges praised the work. They called it a rare open empirical study on core architecture. Expect it in GPT-5, Gemini 2.0, and beyond. Conversations get coherent. Long chats shine.
China Leads NeurIPS Charge
Three of four Best Papers had Chinese lead authors. Alibaba’s win spotlights Asia-Pacific dominance. Co-authors from Edinburgh, Stanford, MIT, Tsinghua add global heft. South China Morning Post notes efficiency gains could transform Qwen’s next gen.
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Why It Matters for AI’s Future
This tweak is dead simple. Sigmoid gate. Post-attention. Done. It fixes pathologies plaguing dense and MoE models alike. Scaling laws strengthen. SLMs benefit too. As NeurIPS notes, adoption looms large. Alibaba proves open research wins.
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