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GRASP: GRouped Activation Shared Parameterization for Parameter-Efficient Fine-Tuning and Robust Inference of Transformers

arXiv — cs.LG•Friday, December 5, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A new framework called GRASP (GRouped Activation Shared Parameterization) has been introduced for parameter-efficient fine-tuning of transformers, allowing for the training of large pre-trained models by updating only a small subset of parameters. This method partitions token representations into groups, learning shared scaling and shifting vectors to enhance model performance while significantly reducing the number of trainable parameters.
  • The development of GRASP is significant as it provides a scalable solution for adapting large language models like RoBERTa and GPT-2 to specific tasks without the need for extensive computational resources. This efficiency can lead to broader accessibility and application of advanced AI models in various fields, enhancing their usability in real-world scenarios.
  • This advancement aligns with ongoing trends in AI research focusing on optimizing model performance while minimizing resource consumption. Techniques such as the Length-MAX tokenizer and adaptive optimizers like AdamHD are also emerging, reflecting a collective effort in the AI community to improve the efficiency and robustness of language models, which are increasingly vital in applications ranging from natural language processing to multimodal tasks.
— via World Pulse Now AI Editorial System
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