IREASONER: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models

Recent work shows that large multimodal models (LMMs) can self-improve from unlabeled data via self-play and intrinsic feedback.Yet existing self-evolving frameworks mainly reward final outcomes, leaving intermediate reasoning weakly constrained despite its importance for visually grounded decision making.We propose IREASONER, a self-evolving framework that improves an LMM's implicit reasoning by explicitly eliciting chain-of-thought (CoT) and rewarding its internal agreement.In a Proposer-Solver loop over unlabeled images, IREASONER augments outcome-level intrinsic rewards with a trajectory-aware signal defined over intermediate reasoning steps, providing learning signals that distinguish reasoning paths leading to the same answer without ground-truth labels or external judges.Starting from Qwen2.5-VL-7B,IREASONER yields up to +2.1 points across diverse multimodal reasoning benchmarks under fully unsupervised post-training.We hope this work serves as a starting point for reasoning-aware selfimprovement in LMMs in purely unsupervised settings.Our code is available here.