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EchoDistill is an alignment-based noisy-to-clean self-distillation framework that improves the robustness of Audio Large Language Models (ALLMs) against real-world noise by using a frozen clean-audio teacher to guide the student model via group-relative policy optimization (GRPO). Experiments show significant improvements in semantic reliability and task performance under strong noise without additional inference costs.