This study focuses on optimizing inference and prediction using the GLM-4 model in multi-agent systems by proposing an innovative approach that integrates generative adversarial networks (GANs) for dynamic denoising with transfer reinforcement learning (TRL). Traditional methods face challenges in noisy data environments, limited model generalization, and poor adaptability in dynamic settings. Rule-based inference methods struggle with environmental variability, and single reinforcement learning models are constrained by sample efficiency and strategy transfer limitations. This research introduces a GAN-based dynamic denoising mechanism in which the generator simulates noise distributions to create pseudo-noise samples, and the discriminator distinguishes between real and noisy data. The adversarial training iteratively enhances data quality. Experiments show that this approach improves the signal-to-noise ratio by over 20%, significantly reducing the interference of noise in GLM-4 inference. Transfer Reinforcement Learning facilitates cross-task knowledge transfer, enhancing learning efficiency in multi-agent collaborative tasks. A state-action value function-based transfer strategy is designed, enabling the migration of learned policy parameters and feature representations from source to target tasks, thereby reducing exploration costs. In multi-agent contract management experiments, this method increased convergence speed by 33% while maintaining policy stability.