Adaptive Sparse Gating: Analysis of a Novel Approach to Transformer Feedforward Layers

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Abstract:We present a comprehensive analysis of Adaptive Sparse Gating (ASG), a novel approach to transformer feedforward layers that incorporates learned sparse activation. While theoretically motivated by computational efficiency considerations, our experiments on the FineWeb benchmark with a Qwen 3 architecture show ASG achieves a loss of 5.11, underperforming the SwiGLU baseline (4.9266). We provide detailed implementation specifics, thorough ablation studies, and analysis of potential failure modes that may inform future research in sparse activation mechanisms.