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Self-Evolving Visual Agent via Margin Reward
Visual agents that interleave reasoning with tool invocation have posed a promising direction for multimodal tasks. However, learning effective tool invocation usually relies on curated question-answer pairs or expert demonstrations at scale, which are expensive to acquire. In this study, we propose Marvo, a simple-yet...
P4Q: Learning to Prompt for Quantization in Low-Bit CLIP
The “Prompt for Quantization” (P4Q) is proposed, by integrating PTQ with Parameter-Efficient Fine-Tuning (PEFT) techniques, and demonstrates that P4Q significantly enhances the performance of low-bit CLIP while reducing deployment costs.