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Merging RL experts effectively requires balancing sharp, informative signals with stable, dispersed components, a challenge that ResMerge addresses with innovative spectral techniques.
Achieve near-lossless performance in autonomous driving VLMs with 90% token reduction – without any training.
LVLMs can run 2.3x faster with only a 2% accuracy drop, thanks to a new pruning method that understands which visual tokens are most relevant to the text.
Decoupling masked reconstruction and contrastive alignment in audio-visual representation learning yields surprisingly large gains in zero-shot retrieval, outperforming SOTA by a significant margin.
