TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
arXiv:2605.31025v1 Announce Type: new Abstract: In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. However, indiscriminately mixing tasks can dilute task specialization, while sequential fine-tuning (full-parameter or low rank adaptation) often causes catastrophic forgetting due to destructive overwriting. Replay-based continual tuning and maintaining separate task-specific adapters can mitigate forgetting, but introduce additional compute, storage, and management over
The proliferation of LLMs in production environments necessitates efficient and robust continual learning methods to manage model evolution without catastrophic forgetting.
Improving continual fine-tuning for LLMs addresses critical performance degradation and resource management challenges inherent in adapting large models to new tasks over time.
New fine-tuning techniques promising to mitigate catastrophic forgetting and reduce compute/storage overhead for LLMs will enable more agile and sustainable AI deployments.
- · AI developers
- · Cloud providers
- · SaaS companies leveraging LLMs
- · Inefficient LLM fine-tuning methods
- · Companies with high compute costs for model updates
More cost-effective and adaptable LLM deployments become feasible across various industries.
Reduced barriers to entry for companies wanting to customize and maintain proprietary LLMs, fostering innovation.
Increased adoption of specialized LLMs for niche tasks, leading to a proliferation of highly capable AI agents.
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Read at arXiv cs.CL