RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning

Guoshenghui Zhao; Huawei Lin; Weijie Zhao

arxiv: 2512.04457 · v2 · pith:PO5O4JZZnew · submitted 2025-12-04 · 💻 cs.CL

RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning

Guoshenghui Zhao , Huawei Lin , Weijie Zhao This is my paper

Pith reviewed 2026-05-21 17:25 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM unlearninginfluence estimationparameter reweightingmachine unlearninglarge language modelsforget setadaptive updates

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The pith

RapidUn enables efficient LLM unlearning by reweighting parameters according to per-sample influence estimates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents RapidUn as a new framework for removing the influence of specific data from large language models. It uses a fast module to estimate how much each sample affects the model parameters and then translates these estimates into weights that adaptively control the unlearning updates. This targeted approach aims to forget unwanted information or behaviors while keeping the rest of the model's knowledge intact. Experiments on Mistral-7B and Llama-3-8B demonstrate substantial speedups over retraining and better results than previous unlearning techniques across different forgetting scenarios.

Core claim

RapidUn is an influence-driven and parameter-efficient unlearning framework. It first estimates per-sample influence through a fast estimation module, then maps these scores into adaptive update weights that guide selective parameter updates -- forgetting harmful behavior while retaining general knowledge.

What carries the argument

The fast influence estimation module and its mapping to adaptive update weights, which directs selective updates to parameters based on each sample's computed influence.

If this is right

Up to 100 times higher efficiency compared to full retraining for unlearning tasks.
Consistent outperformance over Fisher, GA, and LoReUn methods on both in-distribution and out-of-distribution forgetting.
Applicability to models such as Mistral-7B and Llama-3-8B with datasets like Dolly-15k and Alpaca-57k.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the influence estimates are reliable, this method could generalize to unlearning in other AI domains like vision models.
The adaptive weights might offer a way to interpret which parameters are most affected by specific data points.
Future work could test the method on even larger models or more complex forget sets to validate scalability.

Load-bearing premise

The fast influence estimation accurately reflects the true effect of each sample on the model parameters and behavior.

What would settle it

Running the unlearning with the influence scores replaced by random values and observing if performance degrades to match or fall below baseline methods would test the necessity of the influence-driven weighting.

Figures

Figures reproduced from arXiv: 2512.04457 by Guoshenghui Zhao, Huawei Lin, Weijie Zhao.

**Figure 1.** Figure 1: Influence-blind vs. Influence-guided unlearning. (a) Existing approximate methods apply nearly uniform ascent on forget data, often underforgetting or harming clean behavior. (b) RapidUn uses the RapidIn influence estimator to derive bounded per-sample weights that steer LoRA updates, enabling targeted forgetting while preserving utility. Arrow thickness encodes update strength (thicker arrows indicate … view at source ↗

**Figure 2.** Figure 2: Overview of the RapidUn framework. Given a fine-tuned model θ, a small forget set Df , and a retain set Dr, RapidUn (1) estimates sample influences via RapidIn, (2) maps influences to per-sample weights, and (3) performs LoRA-based weighted ascent/descent to remove undesirable behavior while preserving utility. and Dunwanted ⊂ Dtrain the portion whose influence we wish to erase. We are given a small forget… view at source ↗

**Figure 3.** Figure 3: Training dynamics with four methods. Perplexity (PPL) vs. training steps on three evaluation splits for RapidUn, GA, Fisher, and LoReUn. RapidUn achieves the lowest PPL on clean and forget-clean (better retention and recovery) and the highest PPL on forget-poison (stronger suppression). LoReUn is the closest runner-up; GA is intermediate; Fisher converges more slowly and forgets less. Variant Clean PPL (… view at source ↗

**Figure 4.** Figure 4: Aggregate comparison across Llama-3-8B (a) and Mistral-7B (b) on Dolly-15k. Combined radar plots showing normalized performance of all evaluated methods, including approximate unlearning approaches and non-unlearning baselines, on Clean PPL, Seen ASR, and OOD ASR. model yields consistent results ( [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

read the original abstract

Removing specific data influence from large language models (LLMs) remains challenging, as retraining is costly and existing approximate unlearning methods are often unstable. The challenge is exacerbated when the forget set is small or imbalanced. We introduce RapidUn, an influence-driven and parameter-efficient unlearning framework. It first estimates per-sample influence through a fast estimation module, then maps these scores into adaptive update weights that guide selective parameter updates -- forgetting harmful behavior while retaining general knowledge. On Mistral-7B and Llama-3-8B across Dolly-15k and Alpaca-57k, RapidUn achieves up to 100 times higher efficiency than full retraining and consistently outperforms Fisher, GA, and LoReUn on both in-distribution and out-of-distribution forgetting. These results establish influence-guided parameter reweighting as a scalable and interpretable paradigm for LLM unlearning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RapidUn pairs quick influence scoring with adaptive reweighting to cut unlearning cost on 7B-scale models, but the accuracy of those scores against exact baselines is not shown.

read the letter

RapidUn's main move is to estimate per-sample influence quickly, then turn those scores into adaptive weights that steer which parameters get updated during unlearning. The goal is to handle small or skewed forget sets without the cost of full retraining while still keeping general performance intact. They run this on Mistral-7B and Llama-3-8B using Dolly-15k and Alpaca-57k, and report up to 100x speedups plus better results than Fisher, GA, and LoReUn on both in-distribution forgetting and out-of-distribution retention. That framing around influence-guided reweighting for realistic forget-set sizes is the clearest new piece. The efficiency angle and the selective-update design are practical, and testing on current open models gives the claims some immediate relevance. The results section at least shows the method beating the listed baselines across the reported metrics. The main weakness is that the fast estimation module is not checked against exact influence ground truth. There is no reported correlation with full leave-one-out retraining or Hessian-based values on these model sizes, so it is not clear how well the scores actually reflect true parameter effects. Small errors there would directly affect the adaptive weights and could explain or undermine the forgetting and retention numbers, especially on imbalanced data. The abstract also gives no error bars, run-to-run variance, or statistical tests, which leaves the consistency claims hard to judge. This paper is aimed at groups trying to ship data-removal features on production-scale LLMs rather than at theorists. Readers who already work with influence functions or parameter-efficient fine-tuning will see the most direct value. It deserves a serious referee because the efficiency target is important and the pipeline is concrete enough to review in detail.

Referee Report

2 major / 1 minor

Summary. The paper proposes RapidUn, an influence-driven parameter reweighting framework for efficient LLM unlearning. It estimates per-sample influence scores via a fast estimation module and maps them to adaptive update weights to guide selective parameter updates, aiming to forget specific data while retaining general capabilities. Experiments on Mistral-7B and Llama-3-8B with Dolly-15k and Alpaca-57k report up to 100x efficiency gains over full retraining and consistent outperformance versus Fisher, GA, and LoReUn on both in-distribution forgetting and out-of-distribution retention.

Significance. If the fast influence module reliably approximates true per-sample effects, the method could advance practical unlearning for large models by avoiding costly retraining while providing an interpretable reweighting mechanism. The reported efficiency and cross-model results on standard benchmarks indicate potential for scalable applications, though this hinges on unvalidated assumptions about the estimation accuracy.

major comments (2)

[§3] §3 (Fast Influence Estimation Module): The central mapping from estimated scores to adaptive update weights assumes the fast module accurately proxies true influence on model parameters and behavior, but no correlation, ablation, or comparison to exact baselines (full gradients, leave-one-out retraining, or Hessian-based ground truth) is reported for Mistral-7B or Llama-3-8B on the forget sets.
[§4] §4 (Experiments): Claims of consistent outperformance and 100x efficiency lack reported error bars, statistical significance tests, details on random seeds, number of runs, or controls for small/imbalanced forget sets, making it difficult to confirm the results are robust rather than sensitive to particular splits or initializations.

minor comments (1)

[§3.1] The description of how influence scores are normalized or thresholded before mapping to weights could include a small concrete example for clarity.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We appreciate the referee's detailed feedback on our manuscript. We have carefully considered the major comments regarding the validation of our fast influence estimation module and the statistical robustness of our experimental results. Below, we provide point-by-point responses and indicate the revisions we plan to make in the next version of the paper.

read point-by-point responses

Referee: [§3] §3 (Fast Influence Estimation Module): The central mapping from estimated scores to adaptive update weights assumes the fast module accurately proxies true influence on model parameters and behavior, but no correlation, ablation, or comparison to exact baselines (full gradients, leave-one-out retraining, or Hessian-based ground truth) is reported for Mistral-7B or Llama-3-8B on the forget sets.

Authors: We thank the referee for highlighting this important point. Computing exact influence measures such as full gradients or Hessian-based methods for 7B and 8B parameter models on datasets like Dolly-15k is computationally prohibitive, often requiring orders of magnitude more resources than the unlearning process itself. Our fast estimation module builds on established approximations from influence function literature to enable practical application. To address this, we will revise Section 3 to include a more detailed explanation of the module's design rationale, its assumptions, and limitations. This constitutes a partial revision as full-scale exact validations remain infeasible. revision: partial
Referee: [§4] §4 (Experiments): Claims of consistent outperformance and 100x efficiency lack reported error bars, statistical significance tests, details on random seeds, number of runs, or controls for small/imbalanced forget sets, making it difficult to confirm the results are robust rather than sensitive to particular splits or initializations.

Authors: We agree that additional statistical reporting would strengthen the presentation of our results. The experiments were conducted using a fixed random seed for reproducibility across the reported runs, but we acknowledge the value of multiple runs and error bars. In the revised manuscript, we will update Section 4 to report results averaged over multiple random seeds (e.g., 3-5 runs) with standard deviations as error bars where computational resources permit. We will also specify the seeds used, include statistical significance tests (such as paired t-tests) comparing RapidUn to baselines, and add discussion on handling small or imbalanced forget sets, including any controls like balanced sampling or subset-specific metrics. This will be incorporated as a full revision. revision: yes

standing simulated objections not resolved

We cannot provide direct comparisons to exact baselines like Hessian-based ground truth or leave-one-out retraining for the full-scale Mistral-7B and Llama-3-8B models due to prohibitive computational costs.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents RapidUn as an empirical unlearning framework that estimates per-sample influence via a fast module and maps scores to adaptive weights for selective updates. Claims of efficiency (up to 100x vs retraining) and outperformance vs Fisher/GA/LoReUn are supported by direct comparisons on Mistral-7B and Llama-3-8B using Dolly-15k and Alpaca-57k. No steps reduce outputs to inputs by construction, no fitted parameters are relabeled as predictions, and no load-bearing self-citations or uniqueness theorems are invoked. The central method remains independent of its own results and relies on external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

With only the abstract available, the ledger is limited; the method assumes influence functions can be approximated efficiently and that these approximations guide effective forgetting without side effects on retained knowledge.

axioms (1)

domain assumption Per-sample influence can be estimated rapidly without full Hessian or retraining computations
Central to the fast estimation module described in the abstract.

pith-pipeline@v0.9.0 · 5684 in / 1198 out tokens · 85589 ms · 2026-05-21T17:25:47.700276+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

RapidIn estimates per-sample influence via token-wise gradient alignment eI(i→j) and maps to adaptive weights via RobustScale and clipping for weighted ascent/descent

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

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Association for Computational Linguistics. Huawei Lin, Jikai Long, Zhaozhuo Xu, and Weijie Zhao

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poisoned

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In Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers), pages 1–9

BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers), pages 1–9. Association for Computa- tional Linguistics. Kshira Bhaila, Juin-Hwey Hsieh, Mingqing Liu, Nikos Karampatziakis, and Jan Prins. 2025. ...

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IEEE. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, and 1 oth- ers. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33 (NeurIPS), pages 1877–1901. Yinzhi Cao and Junfeng Yang. 2015. Towards making systems forget with machine unlearning. In2015 IEEE Symposium on ...

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Association for Computational Linguistics. Huawei Lin, Jikai Long, Zhaozhuo Xu, and Weijie Zhao

work page

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, Tam, D

Token-wise influential training data retrieval for large language models. InProceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (ACL), pages 841–860. Hao Liu, Derek Tam, and 1 others. 2022. Few- shot parameter-efficient fine-tuning is better and cheaper than in-context learning.arXiv preprint arXiv:2205.05638. Sijia L...

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Available at https://cdn.openai.com/ better-language-models/language_models_ are_unsupervised_multitask_learners.pdf

Language models are unsupervised multitask learners.OpenAI Technical Re- port. Available at https://cdn.openai.com/ better-language-models/language_models_ are_unsupervised_multitask_learners.pdf. Colin Raffel, Noam Shazeer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of tr...

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Concealed data poisoning attacks on NLP models.arXiv preprint arXiv:2010.12563. Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, and Ningyu Zhang. 2025a. Relearn: Unlearning via learning for large language models. InProceedings of the 63rd Annual Meet- ing of the Association for Computational...

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