Recent advances in flow matching models, particularly with reinforcement learning (RL), have significantly enhanced human preference alignment in few step text to image generators. However, existing RL based approaches for flow matching models typically rely on numerous denoising steps, while suffering from sparse and imprecise reward signals that often lead to suboptimal alignment. To address these limitations, we propose Temperature Annealed Few step Sampling with Group Relative Policy Optimization (TAFS GRPO), a novel framework for training flow matching text to image models into efficient few step generators well aligned with human preferences. Our method iteratively injects adaptive temporal noise onto the results of one step samples. By repeatedly annealing the model's sampled outputs, it introduces stochasticity into the sampling process while preserving the semantic integrity of each generated image. Moreover, its step aware advantage integration mechanism combines the GRPO to avoid the need for the differentiable of reward function and provide dense and step specific rewards for stable policy optimization. Extensive experiments demonstrate that TAFS GRPO achieves strong performance in few step text to image generation and significantly improves the alignment of generated images with human preferences. The code and models of this work will be available to facilitate further research.