Boosting RL-Based Visual Reasoning with Selective Adversarial Entropy Intervention 事件

Boosting RL-Based Visual Reasoning with Selective Adversarial Entropy Intervention arXiv:2512.10414v2 Announce Type: replace Abstract: Recently, reinforcement learning (RL) has become a common choice in enhancing the reasoning capabilities of vision-language models (VLMs). Considering existing RL-based finetuning methods, entropy intervention turns out to be an effective way to benefit exploratory ability, thereby improving policy performance. Notably, most existing studies intervene in entropy