This study investigates how interactional characteristics of spoken dialogue corpora influence the learning process and resulting behavior of speech language models for full-duplex dialogue systems. While previous research has mainly focused on improving acoustic and linguistic quality, an effective dialogue system must also capture and reproduce task-dependent interactional dynamics such as conversational tempo and turn-taking patterns. To analyze these properties, we evaluated multiple dialogue corpora using NISQA for speech quality, LLM-as-a-Judge for linguistic and semantic appropriateness, and four timing-based indicators: inter-pausal units, pause, gap, and overlap. A curriculum learning strategy was applied to fine-tune a Moshi-based full-duplex dialogue model by incrementally combining corpora with different interactional characteristics. Experimental results on a dialogue continuation task showed that corpus-specific interactional patterns effectively shape model behavior. Chat-style corpora facilitated natural rhythms with moderate overlaps and gaps, whereas consultation-style corpora promoted more stable and deliberate timing. Fine-tuning with high-quality audio improved speech quality, while using task-mismatched data degraded linguistic coherence.