We introduce AMDKernelVault, an open corpus and training framework for optimizing HIP and Triton kernels on recent AMD CDNA GPUs. Existing LLM-based kernel agents are predominantly designed for CUDA and NVIDIA hardware and often rely on repeated calls to frontier models for generation, reflection, and optimization. To address this gap, we develop HIPKernelGen and TritonKernelGen, agent-driven pipelines that translate PyTorch references into GPU kernels, compile and validate candidates under ROCm, and profile their latency on AMD hardware. AMDKernelVault contains 62,153 execution-verified HIP kernel samples, 2,377 production-grounded ROCm Libraries QA entries, and 39,893 Triton kernels. To demonstrate the corpus’s utility, we specialize Qwen3-8B through supervised fine-tuning and execution-aware reinforcement learning. Under fixed evaluation budgets and identical agent settings, the resulting model achieves the highest correctness among the evaluated models on PyTorch-to-HIP (34.0% Pass@1), TritonBench-G (33.2% Corr@3), and ROCmBench (41.94% Corr@3), while running locally on AMD hardware. These advantages are specific to the reported correctness metrics and do not extend uniformly to compilation accuracy or execution speed. The corpus and documentation are publicly available at https://huggingface.co/datasets/amd/AIG-Datasets.