Scaling laws have enabled predictable compute allocation for pre-training and for RL in reasoning tasks.However, research on retrieval reinforcement generation (RAG) remains insufficient and there is a lack of fundamental understanding of the interaction between retrieval quality and reinforcement learning computation.We present the first systematic study of RL scaling for RAG across three knowledgeintensive benchmarks.We introduce the Retrieval Bottleneck Hypothesis and derive sigmoidal scaling laws showing that retrieval quality, not RL compute, determines the asymptotic performance ceiling.Our analysis reveals three principles: (1) retrieval quality bounds achievable performance, with improving retrieval yielding larger gains than algorithmic innovations; (2) design choices (training objectives, rewards, off-policy methods) primarily modulate compute efficiency, with secondary effects on the ceiling that are substantially smaller than retrieval quality improvements; and (3) stable configurations enable extrapolation with 3.1% error at 4× compute.We further uncover RAG-specific dynamics: optimal document count increases with training, and RL algorithm effectiveness depends critically on retrieval quality.These insights yield RAG-SCALERL, achieving strong performance on knowledge-intensive benchmarks while providing the predictable scaling long available for pre-training but previously absent in RAG-RL.