Alibaba Qwen Secures NeurIPS 2025 Best Paper Award for Attention Mechanisms Breakthrough
Alibaba Qwen has been awarded the prestigious NeurIPS 2025 Best Paper Award for its innovative research on attention mechanisms in large language models. This recognition was announced during the Conference on Neural Information Processing Systems (NeurIPS), a leading event in the fields of machine learning and artificial intelligence.
A Groundbreaking Study on Attention Mechanisms
The awarded paper, titled “Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free,” explores the effects of attention gating on the performance of large models. This research is notable for being the first comprehensive analysis of how attention gating influences training and efficiency in large language models (LLMs).
Understanding Attention Gating
- Attention gating manages the flow of information in neural networks.
- It functions similarly to intelligent noise-canceling headphones, filtering out irrelevant information.
- This mechanism enhances overall model effectiveness.
Research Findings and Methodology
The Qwen team’s rigorous evaluation involved comparing over 30 variants of 15 billion Mixture-of-Experts (MoE) models alongside 1.7 billion dense models. These models were trained on an extensive dataset comprising 3.5 trillion tokens.
The team discovered that a simple adjustment—adding a head-specific sigmoid gate after Scaled Dot-Product Attention—led to significant improvements. Key benefits included:
- Enhanced model performance.
- Improved training stability.
- Support for larger learning rates.
- Better scaling properties.
Impact on Future Models
This innovative approach has been incorporated into the Qwen3-Next model, released in September 2025. The new model replaces conventional attention mechanisms with a hybrid of Gated DeltaNet and Gated Attention, boosting in-context learning capabilities and computational efficiency.
Community Contribution and Future Adoption
To promote further research and encourage adoption of their findings, the Qwen team has made relevant codes and models available on GitHub and HuggingFace. The NeurIPS Selection Committee praised the paper, indicating that the recommendations are easily implementable and anticipate widespread industry adoption.
They emphasized the importance of open sharing in advancing community knowledge surrounding attention mechanisms in LLMs, especially in a landscape where such practices are becoming less common. The contributions from Alibaba Qwen stand as a testament to their commitment to advancing research in artificial intelligence.
