Vision RL

Apply reinforcement learning to vision tasks by using differentiable reward functions (aesthetic scoring, CLIP alignment, or human feedback) to fine-tune generative vision models. Vision RL treats the image generation process as a policy that produces visual outputs, which are then scored by a reward function. The reward can be a learned aesthetic scorer, a CLIP-based alignment score, or a human preference model. By backpropagating through the reward function, the vision model is fine-tuned to produce higher-reward outputs. Key challenges include making non-differentiable rewards differentiable (via score function estimators or Gumbel-softmax) and preventing reward overfitting.