arXiv:2606.15734v1 Announce Type: new Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing catastrophic forgetting and degrading general capabilities. Retrieval-augmented generation avoids such parameter drift, yet often lacks the depth of parametric knowledge integration. In this paper, we propose ReGrad (Retrievable Gradients), a new paradigm that treats gradients as retrievable units of knowledge. ReGrad pre-computes document-specific gradients offline, store
The proliferation of deployed AI models requiring continuous adaptation to new information without degradation necessitates novel approaches to knowledge integration.
This research addresses a fundamental limitation in continual learning for AI, potentially enabling more robust and adaptable intelligent systems that retain long-term memory.
The method of integrating emerging knowledge into AI models shifts from direct weight updates to a retrieval-based gradient system, mitigating catastrophic forgetting.
- · AI developers
- · Enterprises deploying AI
- · Research institutions
- · Users of AI systems
- · Models prone to catastrophic forgetting
- · Current methods reliant solely on fine-tuning
AI models become more adaptive and capable of sustained learning in dynamic environments.
Improved model longevity and reduced retraining costs lead to faster AI adoption in critical applications.
The development of highly specialized, continuously evolving AI agents across various sectors becomes more feasible, potentially accelerating autonomous systems.
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Read at arXiv cs.CL