TILT: MODEL-INTRINSIC REWARD ALIGNMENT FOR COMPOSITIONAL DIFFUSION

Consider conditional generation $p(x\mid C=\{c_1,c_2,\dots c_k\})$ where $C$ is a prompt composed of multiple concepts $c_i$. Diffusion models often struggle with compositional prompts, producing samples in which some concepts dominate while others are missing or weakly represented. Prior work attributes these failures to *mode collision*, where single-concept modes of $p(x\mid c_i)$ overlap with modes of the joint $p(x\mid C)$. To seek out collision-free modes of $p(x\mid C)$, or *pure modes*, corrector-based approaches have attempted to suppress collisions at intermediate diffusion times. However, local corrections are often heuristic and do not necessarily steer the generation to a *pure mode* in the final data space. Derived from a principled formulation, we present TILT (**T**est-time model-**I**ntrinsic reward a**L**ignment via **T**ilting), a training-free framework that poses eventual pure mode sampling as a reward for intermediate-time alignment. This reward offers valuable advantages: (1) it is intrinsic to the model, hence external reward models need not be trained by modality-specific datasets, (2) it yields a closed-form target under a variational approximation, which makes it realizable through standard diffusion sampling, and (3) it is interpretable, hence amenable to preference-based modifications. Project page: [https://tinyurl.com/yr5j3bwe](https://tinyurl.com/yr5j3bwe)