@inproceedings{su-etal-2026-retrievable,
title = "Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift",
author = "Su, Weihang and
Kang, Jiacheng and
Xu, Jingyan and
Ai, Qingyao and
Long, Jianming and
Zhang, Hanwen and
Du, Bangde and
Cao, Xinyuan and
Zhang, Min and
Liu, Yiqun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1197/",
pages = "27407--27426",
ISBN = "979-8-89176-459-0",
abstract = "Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters inevitably accumulates weight drift, which causes catastrophic forgetting and degrades 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 language-modeling gradients are optimized for token-level document reconstruction rather than for query-driven knowledge use. We therefore introduce a bi-level meta-learning objective that reshapes document-derived gradients into generalizable adaptation signals for downstream tasks. Experiments across general and domain-specific settings show that ReGrad outperforms CPT and RAG baselines, enabling scalable and reversible parametric knowledge injection without accumulating weight drift."
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<abstract>Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters inevitably accumulates weight drift, which causes catastrophic forgetting and degrades 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 language-modeling gradients are optimized for token-level document reconstruction rather than for query-driven knowledge use. We therefore introduce a bi-level meta-learning objective that reshapes document-derived gradients into generalizable adaptation signals for downstream tasks. Experiments across general and domain-specific settings show that ReGrad outperforms CPT and RAG baselines, enabling scalable and reversible parametric knowledge injection without accumulating weight drift.</abstract>
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%0 Conference Proceedings
%T Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift
%A Su, Weihang
%A Kang, Jiacheng
%A Xu, Jingyan
%A Ai, Qingyao
%A Long, Jianming
%A Zhang, Hanwen
%A Du, Bangde
%A Cao, Xinyuan
%A Zhang, Min
%A Liu, Yiqun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F su-etal-2026-retrievable
%X Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters inevitably accumulates weight drift, which causes catastrophic forgetting and degrades 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 language-modeling gradients are optimized for token-level document reconstruction rather than for query-driven knowledge use. We therefore introduce a bi-level meta-learning objective that reshapes document-derived gradients into generalizable adaptation signals for downstream tasks. Experiments across general and domain-specific settings show that ReGrad outperforms CPT and RAG baselines, enabling scalable and reversible parametric knowledge injection without accumulating weight drift.
%U https://aclanthology.org/2026.emnlp-main.1197/
%P 27407-27426
Markdown (Informal)
[Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift](https://aclanthology.org/2026.emnlp-main.1197/) (Su et al., EMNLP 2026)
ACL
- Weihang Su, Jiacheng Kang, Jingyan Xu, Qingyao Ai, Jianming Long, Hanwen Zhang, Bangde Du, Xinyuan Cao, Min Zhang, and Yiqun Liu. 2026. Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27407–27426, Budapest, Hungary. Association for Computational Linguistics.