arXiv:2512.18934v2 Announce Type: replace Abstract: Catastrophic forgetting poses a fundamental challenge in continual learning, particularly when models are quantized for deployment efficiency. We systematically investigate the interplay between quantization precision (FP16, INT8, INT4) and replay buffer strategies in large language models, revealing unexpected dynamics. While FP16 achieves superior initial task performance (74.44% on NLU), we observe a striking inversion on subsequent tasks: quantized models outperform FP16 by 8-15% on final task forward accuracy, with INT4 achieving nearly
This research, published in 2026, details advancements in combating catastrophic forgetting in quantized large language models, a persistent challenge in deploying efficient AI. It leverages recent progress in model architectures and quantization techniques to address deployment realities.
This finding fundamentally changes how resource-constrained continual learning systems are designed and deployed, enabling more efficient and adaptive AI models to operate closer to the edge. It suggests that previous assumptions about performance degradation with quantization might be incomplete, opening new avenues for research and application.
The widespread belief that higher precision (e.g., FP16) always leads to superior performance across all tasks, especially in continual learning, is challenged, as quantized models now show unexpected advantages post-initial task. This could shift the development priorities for LLM deployment and hardware optimization.
- · Edge AI providers
- · Hardware manufacturers (specializing in INT8/INT4)
- · AI deployment platforms
- · Developers of resource-constrained AI applications
- · Companies exclusively focused on FP16 inference
- · Cloud-centric LLM providers with high latency requirements
Quantized LLMs will become more prevalent in edge devices and constrained environments due to their improved continual learning capabilities.
This shift will drive further innovation in specialized hardware for INT8/INT4 operations, potentially altering the competitive landscape for AI chip manufacturers.
The enhanced efficiency and adaptability of edge AI units could accelerate the development and adoption of AI agents in distributed and autonomous systems, potentially reducing reliance on centralized compute infrastructure.
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Read at arXiv cs.LG