Pareto LoRA applies Pareto-optimal gradient integration to balance text and image objectives in LoRA-based fine-tuning of unified multimodal models, reporting up to 44.9% gains in image quality on the CoMM benchmark with Emu2 while preserving text performance.
Commit: Coordinated instruction tuning for multimodal large language models.arXiv preprint arXiv:2407.20454, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
Pareto LoRA applies Pareto-optimal gradient integration to balance text and image objectives in LoRA-based fine-tuning of unified multimodal models, reporting up to 44.9% gains in image quality on the CoMM benchmark with Emu2 while preserving text performance.