This paper presents a study on Group Relative Policy Optimization (GRPO) fine-tuning for small language models (SLMs) under a practical compute budget. The study analyzes the effect of group size on policy convergence, training stability, and downstream benchmark performance, and provides practical guidance for GRPO training for SLMs.