DRIFT Framework Aims to Prevent Diversity Collapse in RL Fine-Tuning for Image Generation
A recent paper, identified as arXiv (2601.12401), presents DRIFT (DiveRsity-Incentivized Reinforcement Fine-Tuning for Versatile Image Generation), a framework aimed at addressing the issue of diversity collapse in the reinforcement learning fine-tuning of large generative models like diffusion and flow models. This research, categorized as a replace-cross type, tackles a key challenge where the optimization process leads to a Dirac delta distribution, thereby limiting output diversity. DRIFT promotes diversity during on-policy fine-tuning, balancing effective task alignment with the need for varied outputs, crucial for applications that depend on diverse generations. The study, relevant to the AI and art tech sectors, could influence generative art and creative tools. The paper was released on arXiv, indicating a revision.
Key facts
- Paper ID: arXiv:2601.12401
- Announcement type: replace-cross
- Title: Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation
- Proposes DRIFT framework (DiveRsity-Incentivized Reinforcement Fine-Tuning for Versatile Image Generation)
- Addresses 'curse of diversity collapse' in RL fine-tuning
- Targets large-scale generative models like diffusion and flow models
- Aims to reconcile task alignment with generation diversity
- Examines problem across three representative perspectives
Entities
Institutions
- arXiv