Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees
Inference-time alignment steers diffusion models toward downstream objectives without costly fine-tuning, but existing methods typically optimize expected cost (or reward) without explicitly controlling tail risk. In this work, we propose a risk-averse framework for inference-time alignment of diffusion models that balances expected cost and Conditional Value-at-Risk (CVaR) under trajectory-level Kullback-Leibler regularization. We characterize the optimal path distribution and reverse transitions as functions of the CVaR threshold, reducing the alignment problem to a one-dimensional optimization problem over the threshold. We show that this one-dimensional optimization problem can be non...
| Authors | Yuchen Liang, Xian Yu |
|---|---|
| Primary category | stat.ML |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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