Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model. Although existing methods are derived from different perspectives, we show that many can be written under a common framework, which we call reward score matching (RSM). Under this view, alignment becomes score matching against a value-guided target, and the main differences across methods reduce to the construction of the value-guidance estimator and the effective optimization strength across timesteps. This unification clarifies the bias-variance-compute tradeoffs of existing designs, and distinguishes core optimization componen...
| Authors | Jeongjae Lee, Jinho Chang, Jeongsol Kim et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 26 August 2026 |
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