EraseFlow: Learning concept erasure policies via GFlowNet-driven alignment
The first framework that casts concept unlearning as exploration and optimizes it with GFlowNets.
Erasing harmful or proprietary concepts from powerful text‑to‑image generators is an emerging safety requirement, yet current “concept erasure” techniques either collapse image quality, rely on brittle adversarial losses, or demand prohibitive retraining cycles. We trace these limitations to a myopic view of the denoising trajectories that govern diffusion‑based generation. We introduce EraseFlow, the first framework that casts concept unlearning as exploration in the space of denoising paths and optimizes it with a GFlowNets equipped with the trajectory‑balance objective. By sampling entire trajectories rather than single end states, EraseFlow learns a stochastic policy that steers generation away from target concepts while preserving the model’s prior. EraseFlow eliminates the need for carefully crafted reward models and by doing this, it generalizes effectively to unseen concepts and avoids hackable rewards while improving the performance. Extensive empirical results demonstrate that EraseFlow outperforms existing baselines and achieves an optimal trade-off between performance and prior preservation.
Latest publications
Improving consistency in retrieval-augmented systems with group similarity reward
An RL approach that leverages multiple rollouts across paraphrased set to assign group similarity rewards.
NeurIPSViCrit: a verifiable reinforcement learning proxy task for visual perception in VLMs
An RL proxy task that trains VLMs to localize synthetic hallucinations injected into human-written captions.
NeurIPSSoTA with less: MCTS-guided sample selection for data-efficient visual reasoning self-improvement
Visual reasoning models that achieve SoTA performance using an order of magnitude fewer training samples.
NeurIPS