MoLS scales Adam updates using module-level SNR estimates to correct gradient noise imbalance and improve LLM training convergence and generalization.
Lora: Low-rank adaptation of large language models.ICLR, 1(2):3
4 Pith papers cite this work. Polarity classification is still indexing.
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OSCAR exploits the generative-discriminative gap in LVLMs to build online preference data with MCTS and dual-granularity rewards for DPO-based calibration, claiming SOTA hallucination reduction and improved multimodal performance.
A joint task-model adaptation method learns optimal weights for data selection indicators via ICL proxies on small validation sets, matching or exceeding full-dataset fine-tuning performance with only 30% of samples on GSM8K.
HiP-LoRA decomposes LoRA updates into principal and residual spectral channels with a singular-value-weighted stability budget to reduce forgetting and interference during foundation model adaptation.
citing papers explorer
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Online Self-Calibration Against Hallucination in Vision-Language Models
OSCAR exploits the generative-discriminative gap in LVLMs to build online preference data with MCTS and dual-granularity rewards for DPO-based calibration, claiming SOTA hallucination reduction and improved multimodal performance.