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arxiv: 2605.26454 · v1 · pith:KXX3OE2Qnew · submitted 2026-05-26 · 💻 cs.CL

Model Unlearning Objectives Vary for Distinct Language Functions

Berk Atil , Vipul Gupta , Rebecca J. Passonneau This is my paper

Pith reviewed 2026-06-29 18:35 UTC · model grok-4.3

classification 💻 cs.CL
keywords model unlearningLLM safetydangerous knowledgetoxicitylanguage modelspost-trainingunlearning objectives
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The pith

Unlearning methods must be designed separately for distinct language functions such as dangerous knowledge versus toxicity.

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

The paper argues that unlearning in large language models requires objectives tailored to the specific language function being addressed, mirroring how post-training uses different techniques for different behaviors. It examines two mechanistically distinct cases: removing dangerous knowledge and reducing toxic text generation. A cosine-based meta-learned variant of RMU is developed for the first goal, while a multi-layer objective using layer-specific probe directions handles the second. Experiments on four open-source 7-8B models show these specialized approaches succeed, supporting the broader claim that unlearning forms a family of problems rather than one unified task. A reader would care because this points to more precise ways to mitigate separate risks without a single catch-all method.

Core claim

We argue that unlearning methods should be designed for the language function at issue. To study this, we consider two mechanistically distinct unlearning goals, dangerous-knowledge unlearning and toxicity unlearning. For dangerous knowledge, we introduce a cosine-based, meta-learned variant of RMU. For toxicity, we propose a multi-layer objective based on layer-specific probe directions. Across four open-source 7-8B models, our methods achieve strong results, based on distinct training objectives for the two types of unlearning. Overall, our results suggest that unlearning should be studied as a family of problems, analogous to the multiple types of LLM post-training.

What carries the argument

Distinct training objectives for different unlearning goals: a cosine-based meta-learned RMU for dangerous knowledge and a multi-layer probe objective for toxicity.

If this is right

  • Unlearning techniques developed for one goal will not transfer directly to the other.
  • Research should treat unlearning as multiple specialized problems rather than seeking a universal method.
  • Benchmarks and evaluations for unlearning success should account for the distinct mechanisms involved.
  • Post-training analogies imply that families of unlearning techniques will continue to develop.

Where Pith is reading between the lines

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

  • Safety engineering may shift toward modular toolkits of unlearning methods for different risk categories.
  • The distinction could apply to additional functions such as reducing hallucinations or enforcing specific factual constraints.
  • Testing whether the two objectives interfere when applied together would clarify practical deployment limits.

Load-bearing premise

That dangerous-knowledge unlearning and toxicity unlearning are mechanistically distinct enough to require entirely separate objectives.

What would settle it

Showing that the cosine-based RMU for dangerous knowledge performs as well on toxicity reduction as the multi-layer probe method does, or vice versa, without any adaptation.

Figures

Figures reproduced from arXiv: 2605.26454 by Berk Atil, Rebecca J. Passonneau, Vipul Gupta.

Figure 1
Figure 1. Figure 1: Cosine similarities between logistic-regression probes trained at different layers of Llama3.1-8B [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Weight distribution of the logistic-regression probe trained on layer 11 of Llama3.1-8B for toxicity classification. is fixed throughout training. We found this to be a limitation, because we observed the best value of α to vary substantially across models and datasets. To address this, we treat α as a learnable param￾eter and update it during fine-tuning using REIN￾FORCE (Williams, 1992). At each step, th… view at source ↗
Figure 3
Figure 3. Figure 3: Dangerous knowledge unlearning results for the four models. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Toxicity unlearning results for the four models. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Toxicity unlearning loss curves for each model. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The effect of number and identity of the layers on toxicity unlearning and general capability 6.1 Toxicity Probe Analysis To better understand the internal geometry of toxi￾city representations and obtain principled unlearn￾ing directions, we train logistic regression probes at multiple layers of Llama-3.1-8B and analyze the pairwise cosine similarity between the learned weight vectors [PITH_FULL_IMAGE:fi… view at source ↗
Figure 7
Figure 7. Figure 7: The effect of number of layers on toxicity unlearn￾ing B Hyperparameters and Computing Infrastructure We have finetuned the models for two epochs on up to 4 Nvidia RTX A6000. Each run took about an hour. For the reinforcement learning, we used a learning rate of 1e-2, and for the main finetuning, we used 5e-5. We used Adam optimizer for all experiments. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original abstract

Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-training uses different objectives to shape different behaviors, we argue that unlearning methods should be designed for the language function at issue. To study this, we consider two mechanistically distinct unlearning goals, dangerous-knowledge unlearning and toxicity unlearning. For dangerous knowledge, we introduce a cosine-based, meta-learned variant of RMU. For toxicity, we propose a multi-layer objective based on layer-specific probe directions. Across four open-source 7-8B models, our methods achieve strong results, based on distinct training objectives for the two types of unlearning. Overall, our results suggest that unlearning should be studied as a family of problems, analogous to the multiple types of LLM post-training.

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.

Referee Report

2 major / 1 minor

Summary. The paper claims that LLM unlearning requires distinct objectives tailored to specific language functions, as dangerous-knowledge unlearning and toxicity unlearning are mechanistically distinct. It introduces a cosine-based meta-learned variant of RMU for the former and a multi-layer probe objective for the latter, reporting strong results across four 7-8B open-source models and concluding that unlearning should be studied as a family of problems analogous to post-training objectives.

Significance. If the central empirical claim holds after addressing the experimental gaps, the work would provide a useful framing for unlearning research by highlighting the need for function-specific methods rather than generic approaches. The introduction of task-tailored techniques (meta-learned RMU and layer-specific probes) offers concrete starting points for future specialization, though the current evidence does not yet establish that these are non-interchangeable.

major comments (2)
  1. [Experimental results] Experimental results section: No cross-task ablation is reported in which the cosine-based meta-learned RMU is applied to toxicity unlearning or the multi-layer probe is applied to dangerous-knowledge unlearning. Without these comparisons, the observed performance cannot distinguish between method specialization (supporting the claim of distinct objectives) and the possibility that both techniques are broadly effective unlearning methods; this directly undermines the load-bearing assertion that separate families of objectives are required.
  2. [Methods and results] Methods and results: The abstract and methods describe 'strong results' but the provided text gives no quantitative metrics, baselines, statistical tests, or controls for post-hoc selection; without these details it is impossible to assess whether the reported gains are robust or task-specific.
minor comments (1)
  1. [Abstract] Abstract: Lacks any mention of specific evaluation metrics, dataset sizes, or statistical significance, making the 'strong results' claim difficult to interpret without reading the full experimental section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight important ways to strengthen the evidence for our central claim. We respond to each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Experimental results] Experimental results section: No cross-task ablation is reported in which the cosine-based meta-learned RMU is applied to toxicity unlearning or the multi-layer probe is applied to dangerous-knowledge unlearning. Without these comparisons, the observed performance cannot distinguish between method specialization (supporting the claim of distinct objectives) and the possibility that both techniques are broadly effective unlearning methods; this directly undermines the load-bearing assertion that separate families of objectives are required.

    Authors: We agree this is a substantive gap. The manuscript demonstrates that each method achieves strong results on its intended task and is motivated by the distinct mechanisms of dangerous-knowledge versus toxicity unlearning, but without the requested cross-task ablations it is not possible to rule out that the techniques could be interchangeable. We will add the cross-task experiments (or, if compute constraints prevent full runs, a clear discussion of the limitation and planned follow-up) in the revised version. revision: yes

  2. Referee: [Methods and results] Methods and results: The abstract and methods describe 'strong results' but the provided text gives no quantitative metrics, baselines, statistical tests, or controls for post-hoc selection; without these details it is impossible to assess whether the reported gains are robust or task-specific.

    Authors: The full manuscript reports quantitative metrics, baseline comparisons (including standard RMU and other unlearning approaches), and evaluation details across the four models. We will revise the abstract to include key numerical results and expand the methods section to explicitly describe statistical tests, variance reporting, and controls against post-hoc selection. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes two new unlearning methods (cosine-based meta-learned RMU for dangerous knowledge; multi-layer probe for toxicity) and reports empirical results on four models. Claims rest on experimental performance rather than any derivation that reduces by construction to inputs, self-definitions, or self-citation chains. No equations or steps in the abstract or described content exhibit the enumerated circular patterns; the argument for task-specific objectives is framed as an empirical suggestion, not a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the unstated assumption that the two target behaviors are mechanistically separable and that performance gains come from objective specialization rather than other factors; no free parameters, axioms, or invented entities are explicitly listed in the abstract.

pith-pipeline@v0.9.1-grok · 5667 in / 1154 out tokens · 21275 ms · 2026-06-29T18:35:01.279273+00:00 · methodology

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Reference graph

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