Correlation-Weighted Multi-Reward Optimization for Compositional Generation

Text-to-image models produce images that align well withnatural language prompts, but compositional generation has long beena central challenge. Models often struggle to satisfy multiple conceptswithin a single prompt, frequently omitting some concepts and resultingin partial success. Such failures highlight the difficulty of jointly op-timizing multiple concepts during reward optimization, where compet-ing concepts can interfere with one another. To address this limitation,we propose Correlation-Weighted Multi-Reward Optimization (CMO),a framework that leverages the correlation structure among concept re-wards to adaptively weight each attribute concept in optimization. Byaccounting for interactions among concepts, CMO balances competingreward signals and emphasizes concepts that are partially satisfied yetinconsistently generated across samples, improving compositional gener-ation. Specifically, we decompose multi-concept prompts into pre-definedconcept groups (e.g., objects, attributes, and relations) and obtain re-ward signals from dedicated reward models for each concept. We thenadaptively reweight these rewards, assigning higher weights to conflictingor hard-to-satisfy concepts using correlation-based difficulty estimation.By focusing optimization on the most challenging concepts within eachgroup, CMO encourages the model to consistently satisfy all requestedattributes simultaneously. We apply our approach to train state-of-the-art diffusion models, SD3.5 and FLUX.1-dev, and demonstrate consis-tent improvements on challenging multi-concept benchmarks, includingConceptMix, GenEval 2, and T2I-CompBench.