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 stud- ies intervene in entropy by simply controlling the update of specific tokens during policy optimization of RL. They ig- nore the entropy intervention during the RL sampling that can boost the performance of GRPO by improving the di- versity of responses. In this paper, we propose Selective- adversarial Entropy Intervention, namely SaEI, which en- hances policy entropy by distorting the visual input with the token-selective adversarial objective coming from the en- tropy of sampled responses. Specifically, we first propose entropy-guided adversarial sampling (EgAS) that formu- lates the entropy of sampled responses as an adversarial ob- jective. Then, the corresponding adversarial gradient can be used to attack the visual input for producing adversarial samples, allowing the policy model to explore a larger an- swer space during RL sampling. Then, we propose token- selective entropy computation (TsEC) to maximize the ef- fectiveness of adversarial attack in EgAS without distorting factual knowledge within VLMs. Extensive experiments on both in-domain and out-of-domain datasets show that our proposed method can greatly improve policy exploration via entropy intervention, to boost reasoning capabilities. Code will be released once the paper is accepted.
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