A new post-training method uses multi-metric perceptual rewards to optimize speech enhancement models. It directly applies non-differentiable metrics like DNSMOS, WER, and UTMOS as rewards via Group Sequence Policy Optimization, achieving state-of-the-art results on DNS2020. Human evaluation confirms that combining multiple metrics outperforms single-metric approaches, reducing reward hacking.