Gradient-based proportional LoRA rank allocation under GRPO reduces accuracy by 4.5 points versus uniform allocation because GRPO gradients are flatter across layers and non-uniform ranks amplify importance differences.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Omni-Embed-Audio uses multimodal LLMs to match CLAP on standard audio retrieval while improving text-to-text retrieval by 22% relative and hard negative discrimination by 4.3 points HNSR@10 on user-intent queries.
Llama 3.1 8B fine-tuned with calibrated 5% synthetic data augmentation reaches 0.6234 F1-macro on multi-class toxicity detection in gaming chat and places fourth among 35 teams.
citing papers explorer
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Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study
Gradient-based proportional LoRA rank allocation under GRPO reduces accuracy by 4.5 points versus uniform allocation because GRPO gradients are flatter across layers and non-uniform ranks amplify importance differences.
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Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
Omni-Embed-Audio uses multimodal LLMs to match CLAP on standard audio retrieval while improving text-to-text retrieval by 22% relative and hard negative discrimination by 4.3 points HNSR@10 on user-intent queries.
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PSK@EEUCA 2026: Fine-Tuning Large Language Models with Synthetic Data Augmentation for Multi-Class Toxicity Detection in Gaming Chat
Llama 3.1 8B fine-tuned with calibrated 5% synthetic data augmentation reaches 0.6234 F1-macro on multi-class toxicity detection in gaming chat and places fourth among 35 teams.