This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2025 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages, in several underlying frameworks, and datasets are given with or without sentence segmentation. This year, for the ranked, closed track, the campaign adds as a constraint to train only one model for each task, with an upper bound on the size of the model (no more than 4B parameters).An additional open track authorizes any size of, possibly non public, models that will not be reproduced by the organizers and thus not ranked.We compared several fine-tuning approaches either based on encoder-only transformer-based models, or auto-regressive generative ones. To be able to train one model on the variety of corpora, we explored various ways of combining data – by framework, language or language groups, with different sequential orderings –, and the addition of features to guide the model. For the closed track, our final submitted system is based on XLM-RoBERTa large for relation identification, and on InfoXLM for segmentation and connective identification. Our experiments demonstrate that building a single, multilingual model does not necessarily degrade the performance compared to language-specific systems, with at best 64.06% for relation identification, 90.19% for segmentation and 81.15% for connective identification (on average on the development sets), results that are similar or higher that the ones obtained in previous campaigns.We also found that a generative approach could give even higher results on relation identification, with at best 64.65% on the dev sets.