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iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 16:18:24 UTC.

Briefings

This research addresses the trade-off between alignment and diversity in diffusion policy optimization using reinforcement learning. The authors demonstrate that late-timestep updates can harm model diversity, proposing an incremental Feynman-Kac training method to achieve better alignment-diversity balance. Extensive experiments across three tasks and ablation studies validate significant performance gains.

Key points

• DDPO-based reinforcement learning optimizes reverse diffusion processes but currently sacrifices diversity and quality during reward optimization.
• The study mathematically proves that late-timestep updates in diffusion models can negatively impact diversity, contradicting prior conclusions.

Context

Diffusion policy optimization (DDPO) uses reinforcement learning to improve alignment in generative models, but existing methods often compromise model diversity. This work provides a theoretical framework to balance these competing objectives.

Limitations

The study covers three different tasks with ablation studies but lacks long-term evaluation data. Results are based on limited experimental scope and theoretical analysis rather than comprehensive empirical validation.

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