EtCon: Edit-then-Consolidate for Reliable Knowledge Editing
Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, proving effective for making selective edits. However, a significant gap exists between their performance in controlled, teacher-forcing evaluations and their real-world effectiveness in lifelong learning scenarios, which greatly limits their practical applicability. This work's empirical analysis reveals two recurring issues associated with this gap: (1) Most traditional methods lead the edited model to overfit to the new fact, thereby degrading pre-trained capabilities; (2) There is a critical absence of a knowledge consolidation stage, leaving new facts insufficiently integrated into LLMs' inference-time behavior under autoregressive generation, thereby leading to a mismatch between parametric knowledge and actual generation behavior. To this end, we propose Edit-then-Consolidate, a novel knowledge editing paradigm that aims to bridge the gap between theoretical knowledge editing methods and their real-world applicability. Specifically, (1) our framework mitigates overfitting via Targeted Proximal Supervised Fine-Tuning (TPSFT) that localizes the edit via a trust-region objective to limit policy drift; (2) Then, a consolidation stage using Group Relative Policy Optimization (GRPO) aligns the edited knowledge with CoT-based inference policy by optimizing trajectory-level behavior under comprehensive reward signals. Extensive experiments demonstrate our framework consistently improves editing reliability and generalization under real-world evaluations, while better preserving locality and pre-trained capabilities.
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Ruilin Li
693a66ff74fced5bf9c324dc
Yibin Wang
admin_assigned
2025-12-11T10:49:48.784Z
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SII-Yibin Wang
CodeGoat24
user
CodeGoat24
693a66ff74fced5bf9c324dd
Wenhong Zhu
693a66ff74fced5bf9c324de
Chenglin Li
693a66ff74fced5bf9c324df
Jinghao Zhang
693a66ff74fced5bf9c324e0
Chenliang Li
693a66ff74fced5bf9c324e1
Junchi Yan
admin_assigned
2025-12-11T10:50:22.116Z
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Junchi Yan
Rethinker
user
Rethinker
693a66ff74fced5bf9c324e2
Jiaqi Wang
claimed_verified
2026-02-17T15:49:16.963Z
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Jiaqi Wang
myownskyW7
user
myownskyW7
EtCon: Edit-then-Consolidate for Reliable Knowledge Editing
Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, proving effective for making selective edits. However, a significant gap exists between their performance in controlled, teacher-forcing evaluations and their real-world effectiveness in lifelong learning scenarios, which greatly limits their practical applicability. This work's empirical analysis reveals two recurring issues associated with this gap: (1) Most traditional methods lead the edited model to overfit to the new fact, thereby degrading pre-trained capabilities; (2) There is a critical absence of a knowledge consolidation stage, leaving new facts insufficiently integrated into LLMs' inference-time behavior under autoregressive generation, thereby leading to a mismatch between parametric knowledge and actual generation behavior. To this end, we propose Edit-then-Consolidate, a novel knowledge editing paradigm that aims to bridge the gap between theoretical knowledge editing methods and their real-world applicability. Specifically, (1) our framework mitigates overfitting via Targeted Proximal Supervised Fine-Tuning (TPSFT) that localizes the edit via a trust-region objective to limit policy drift; (2) Then, a consolidation stage using Group Relative Policy Optimization (GRPO) aligns the edited knowledge with CoT-based inference policy by optimizing trajectory-level behavior under comprehensive reward signals. Extensive experiments demonstrate our framework consistently improves editing reliability and generalization under real-world evaluations, while better preserving locality and pre-trained capabilities.
A novel knowledge editing framework, Edit-then-Consolidate, addresses overfitting and lack of knowledge integration in large language models through targeted fine-tuning and policy optimization, enhancing reliability and generalization.
knowledge editing large language models teacher-forcing evaluations lifelong learning overfitting knowledge consolidation Targeted Proximal Supervised Fine-Tuning trust-region objective policy drift Group Relative Policy Optimization trajectory-level behavior comprehensive reward signals CoT-based inference policy