GAD-RL: adaptive gated distillation to improve OCR faithfulness
The paper (arXiv:2609.38282v1) introduces GAD-RL, a method that adaptively gates and attenuates teacher distillation during joint post-training to improve OCR faithfulness in vision–language models. On Qwen3.5-2B, GAD-RL disables distillation for high-reward response groups, weights forward KL by the student's probability of the teacher's top token, and achieves 59.92% Micro Recall on CHAOS-Bench (beating GRPO and GRPO+OPD by 8.45 and 4.43 points) and an Overall score of 91.18 on OmniDocBench v1.6.