This thesis addresses the challenge of initial frame noise in real-time ray tracing when using ReSTIR. We propose and evaluate an approach that integrates a normal- aware hash grid for precomputed reservoir caching to improve direct illumination. The research investigates how reservoir caching enhances visual quality alongside ReSTIR and analyzes the associated trade-offs in memory usage and performance. Our con- tribution includes the implementation and analysis of this caching strategy, assessing its impact on visual fidelity across diverse scenes. Although the method significantly reduces noise and improves initial sampling convergence, it can introduce visible grid artifacts in scenes with many light overlaps. Furthermore, this approach incurs notable memory overhead and increased frame times. This work demonstrates the potential of normal-aware hash grids for ReSTIR improvements, providing a proof-of-concept algorithm for stable, high-quality initial samples.