When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On

Researchers propose Implicit Error Counting (IEC), a new reinforcement learning approach for training AI models in domains where multiple valid outputs exist and traditional rubric-based evaluation fails. The method focuses on counting what responses get wrong rather than what they get right, with validation shown in virtual try-on applications where it outperforms existing rubric-based methods.