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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UNVERDICTED 4representative citing papers
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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Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio
MoLS scales Adam updates using module-level SNR estimates to correct gradient noise imbalance and improve LLM training convergence and generalization.
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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.
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Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies
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.
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HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation
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.