Artificial neural networks (ANNs) offer a computationally efficient alternative to conventional physics-based satellite retrievals of atmospheric methane (CH₄). However, the impact of target design on retrieval generalization and robustness remains unclear. Here, we develop two ANN-based column-averaged dry-air mole fraction of methane (XCH₄) retrieval frameworks from GOSAT-2 observations. The models are trained with full-physics (ANN-FP) or proxy (ANN-Proxy) retrieval products and evaluated against 2021–2022 observations and TCCON measurements. While both ANNs reproduce their training targets during training, ANN-Proxy is more stable out of sample, whereas ANN-FP shows increasing bias and drift. Mechanistic analyses using feature attribution, local gradient geometry, and environmentally conditioned perturbation modes indicate that ANN-Proxy concentrates over 97 % of its attribution within CH₄ and carbon dioxide (CO₂) absorption windows and maintains a coherent sensitivity field (mean gradient-direction similarity 0.96 versus 0.72 for ANN-FP). Furthermore, ANN-Proxy sensitivity directions are less aligned with aerosol- and albedo-induced perturbation modes, indicating reduced environmental coupling. The proxy-oriented target shapes the learned mapping to enhance robustness and transferability. These results suggest that physically structured proxy targets provide a practical strategy for rapid, stable, and operationally deployable neural-network methane retrievals in future carbon-monitoring satellite missions.