Rafal Litka focused on stabilizing FP8 quantization within the intel/neural-compressor repository, addressing a regression in the PatchedKVCache module that affected inference reliability. He identified issues in the delegation and cache fetch logic, where patched modules failed to correctly call the original forward and fetch_from_cache methods, leading to instability in FP8 model paths. Rafal implemented a targeted fix in Python using PyTorch, ensuring proper delegation and improving both inference stability and code maintainability. His work demonstrated depth in deep learning and model quantization, resolving a complex bug and reducing variance in production workloads for FP8 quantized models.