MSP: Modality Self-Play (arXiv:2610.20965v1) enables multimodal LLM training without paired cross-modal data
The paper proposes Modality Self-Play (MSP), a training framework that uses model-generated proxy textual descriptions as semantic bridges to align and compose modalities without requiring real paired non-text data. MSP combines anchor modality inputs, proxy descriptions, pretrained contrastive encoders, and learned modality-distribution bridges to enable zero-shot cross-modal composition and achieves state-of-the-art or competitive results on multiple multimodal benchmarks.