Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models
Xuanming Cui, Shlok Kumar Mishra, Wentao Bao +6
Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods eit…
cs.LG2026
ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning
Ruizhong Qiu, Hanqing Zeng, Yinglong Xia +15
Low-rank adapters (LoRAs) are a parameter-efficient finetuning technique that injects trainable low-rank matrices into pretrained models to adapt them to new tasks. Mixture-of-LoRA…