Faithful Warm-Start pre-training on causally consistent vision-language samples improves accuracy, stabilizes RL, and reduces unsupported reasoning in VLMs.
arXiv preprint arXiv:2603.19532 , year=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning
Faithful Warm-Start pre-training on causally consistent vision-language samples improves accuracy, stabilizes RL, and reduces unsupported reasoning in VLMs.