Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift
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, stores them in an indexed Gradient Bank, and retrieves only query-relevant gradients at inference time for temporary weight adaptation. However, raw l...
| Authors | Weihang Su, Jiacheng Kang, Jingyan Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 11 October 2026 |
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