Guide the agent through implementing a correct, numerically stable RMSNorm kernel in Triton: `y = x * rsqrt(mean(x², axis=-1) + eps) * weight`. RMSNorm is the dominant normalization in modern decoder-only LLMs (LLaMA, Mistral, Qwen, Gemma, DeepSeek). This skill covers one-pass sum-of-squares with fp32 accumulation, the persistent kernel pattern when the hidden dim fits in a single tile, masking for non-divisible tails, the affine weight broadcast (no bias), and the backward pass. RMSNorm is structurally simpler than LayerNorm — no mean subtraction, no Welford — but the failure modes around fp16 squaring and weight pointer arithmetic still bite.
