GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning

Researchers introduce GRID, a framework addressing scalability and task-agnostic inference challenges in continual prompt tuning for large language models. The method combines output-aware decoding with gradient-guided prompt selection to improve backward transfer while reducing memory consumption across multiple LLM architectures.

GRID tackles a fundamental limitation in prompt-based continual learning: existing systems degrade significantly when task identifiers are unavailable at inference time, forcing reliance on task-aware inference that limits real-world deployment. This research advances the field by decoupling inference from explicit task knowledge, enabling more practical AI systems that must operate across evolving task sequences without prior knowledge of which task a given input belongs to.

The core innovation combines two complementary mechanisms. The output-space-aware decoding mechanism leverages representative inputs and semantic label normalization to strengthen backward transfer—the ability to retain performance on previously learned tasks while adding new ones. Simultaneously, the gradient-guided prompt selection strategy compresses redundant task-specific prompts into aggregated representations, directly addressing scalability constraints that plague existing continual learning approaches as task sequences grow longer.

For the AI development ecosystem, GRID's parameter-efficient approach has significant implications. By reducing memory footprint across encoder-decoder models like T5 and decoder-only architectures like Qwen and LLaMA, the framework makes continual learning more accessible for resource-constrained deployments. This efficiency gain becomes critical as organizations build systems that must continuously adapt to new domains without retraining base models.

The competitive forward transfer performance combined with improved backward transfer suggests GRID achieves better balance than prior methods. As continual learning becomes increasingly important for production LLM systems—particularly in enterprises managing multiple specialized tasks—this framework provides practical tools for addressing the memory and inference scalability challenges that have hindered mainstream adoption. Future research may explore whether these techniques extend to multimodal models or larger-scale task sequences.

• →GRID enables task-agnostic inference in continual learning, eliminating performance degradation when task identifiers are unavailable
• →Gradient-guided prompt selection compresses task-specific prompts into aggregated representations, substantially reducing memory requirements
• →Output-space-aware decoding with semantic normalization improves backward transfer across long task sequences
• →Framework maintains competitive forward transfer while achieving superior backward transfer compared to existing methods
• →Demonstrated effectiveness across multiple architectures including T5, Qwen, and LLaMA indicates broad applicability