Generative List Recommendation (GLR) models have shown superior performance in E-commerce by directly generating high-quality recommendation lists through sequential item selection. While Online Learning (OL) has proven valuable for streaming point-wise recommendation models in adapting to dynamic user preferences, its application to GLR remains largely unexplored, due in large part to the inefficiency and instability of conventional on-policy reinforcement learning algorithms used in existing GLR approaches. Existing approaches typically rely on surrogate losses, which provide indirect and biased gradient estimates, making them ill-suited for the rapid, subtle distribution shifts common in real-world E-commerce environments. In this paper, we propose Environment Policy Optimization (EPO), a novel GLR model that fundamentally reshapes policy learning by exploiting the differentiability of the environment within the Generator-Evaluator framework. EPO recognizes that the evaluator is a neural network, capable of providing gradient signals. By directly utilizing these gradients, EPO enables end-to-end optimization of the total list-wise reward—the true objective. To ensure differentiable list generation, EPO introduces a new indexing and exploration strategy based on NeuralSort and Gumbel noise, which relaxes discrete item selection into a continuous, gradient-friendly operation. EPO not only demonstrates strong performance in offline evaluations but also unlocks the potential of online learning for GLR at an industrial scale, yielding a 1.18% relative improvement in user clicks in online A/B tests. The results underscore the critical role of EPO in providing the sensitivity, stability, and gradient fidelity necessary for effective real-time adaptation in streaming and dynamic recommendation environments. Moreover, EPO reached the baseline performance with a training time deduction of 76% under identical hardware conditions.