arxiv: 2604.21251 · v4 · submitted 2026-04-23 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

CAP: Controllable Alignment Prompting for Unlearning in LLMs

Zhaokun Wang , Jinyu Guo , Jingwen Pu , Hongli Pu , Meng Yang , Xunlei Chen , Jie Ou , Wenyi Li

show 2 more authors

Guangchun Luo Wenhong Tian

Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:59 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords unlearninglarge language modelsprompt optimizationreinforcement learningknowledge suppressionreversible alignmentparameter-free methodscontrollable forgetting

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The pith

CAP trains a prompt generator with reinforcement learning to suppress targeted knowledge in LLMs without updating any model parameters.

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

The paper proposes the CAP framework as a non-invasive method for unlearning specific knowledge in large language models. It decouples the unlearning task into an optimization process where a prompt generator, trained via reinforcement learning, creates alignment prompts that collaborate with the frozen LLM to suppress sensitive information while retaining general capabilities. The approach is designed to be fully reversible: revoking the prompt restores the original behavior. This addresses core limitations of prior techniques, including high computational costs, uncontrollable forgetting boundaries, and the requirement for direct weight access that makes them unusable on closed-source models. A reader focused on practical AI safety would see value in a method that enables dynamic, controllable unlearning without permanent model changes.

Core claim

CAP decouples unlearning into a learnable prompt optimization process via reinforcement learning, where a prompt generator collaborates with the LLM to suppress target knowledge while preserving general capabilities selectively. This establishes a dynamic alignment mechanism that overcomes the transferability limitations of prior methods and enables reversible knowledge restoration through prompt revocation.

What carries the argument

A reinforcement-learned prompt generator that produces alignment prompts to selectively suppress target knowledge in the frozen LLM.

If this is right

Unlearning becomes feasible for closed-source models that provide no weight access.
Forgetting boundaries can be adjusted dynamically by changing or removing the prompt.
Knowledge can be restored instantly without any retraining or additional computation.
General model capabilities remain intact during the unlearning process.
The method provides a transferable alignment mechanism independent of specific model architectures.

Where Pith is reading between the lines

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

The same prompt-optimization loop could extend to other forms of runtime behavioral control, such as context-specific safety filters.
It suggests a path toward modular alignment where different prompt generators handle different regulatory or user-specific constraints without model duplication.
Integration with existing prompt-tuning pipelines might allow unlearning to be applied selectively across user sessions or deployment environments.
The approach raises the possibility of reversible unlearning as a standard feature in production LLM serving systems.

Load-bearing premise

Reinforcement learning can train a prompt generator to suppress only the intended target knowledge without unintended degradation of the LLM's general performance, and that effect disappears completely when the prompt is removed.

What would settle it

An experiment showing that the target knowledge remains inaccessible or general capabilities stay degraded even after the learned prompt is fully revoked.

Figures

Figures reproduced from arXiv: 2604.21251 by Guangchun Luo, Hongli Pu, Jie Ou, Jingwen Pu, Jinyu Guo, Meng Yang, Wenhong Tian, Wenyi Li, Xunlei Chen, Zhaokun Wang.

**Figure 2.** Figure 2: The CAP pipeline consists of two stages: Prompt Generator Optimization and Inference Stage. Dual [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Visualization Example of B-PPO. During inference, the SLM generates multiple candidate prompts for the input query. The SelfCheck instruction then selects or slightly refines the most appropriate candidate to guide the final output. More implementation details of the SelfCheck instruction are provided in Appendix H.1.3. 4 Experiments 4.1 Experimental Settings Datasets. To evaluate the method’s ability to… view at source ↗

**Figure 4.** Figure 4: Comparison of the attention matrix before and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Comparison of the forgetting prompt guidance [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: ROUGE-L recall comparison of unlearning methods with and without adversarial prompts. 5.2 Visualization of Hidden State Shift Although CAP effectively reduces accuracy on sensitive questions, a critical question remains: Does it disrupt semantic understanding or redirect semantics toward an ignorance region? To investigate, we extracted hidden states from each layer of LLaMA2-7B when processing sensitiv… view at source ↗

**Figure 7.** Figure 7: Comparison of the same sentence with or without our prompt. that the generated prefix functions as a semantic anchor that redirects internal activations from knowledge regions toward safe/refusal regions, rather than merely introducing noise. This representationlevel separation explains how CAP achieves deep unlearning while preserving linguistic fluency. 6 Conclusion We present CAP, an end-to-end promp… view at source ↗

read the original abstract

Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. However, existing parameter-modifying methods face fundamental limitations: high computational costs, uncontrollable forgetting boundaries, and strict dependency on model weight access. These constraints render them impractical for closed-source models, yet current non-invasive alternatives remain unsystematic and reliant on empirical experience. To address these challenges, we propose the Controllable Alignment Prompting for Unlearning (CAP) framework, an end-to-end prompt-driven unlearning paradigm. CAP decouples unlearning into a learnable prompt optimization process via reinforcement learning, where a prompt generator collaborates with the LLM to suppress target knowledge while preserving general capabilities selectively. This approach enables reversible knowledge restoration through prompt revocation. Extensive experiments demonstrate that CAP achieves precise, controllable unlearning without updating model parameters, establishing a dynamic alignment mechanism that overcomes the transferability limitations of prior methods.

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

CAP tries to unlearn specific knowledge in LLMs by training an RL prompt generator instead of editing weights, but the experiments do not clearly rule out side effects on related capabilities.

read the letter

The core idea is to optimize prompts via reinforcement learning so the model suppresses target facts on command while leaving the rest of its behavior intact, and to make that suppression go away when the prompt is removed. This targets the practical problem of closed-source models where weight access is impossible and parameter-based unlearning is off the table. The framing as an end-to-end learnable prompt process is the main technical step beyond earlier hand-crafted prompt attempts.

Referee Report

1 major / 0 minor

Summary. The paper proposes the Controllable Alignment Prompting (CAP) framework for unlearning in LLMs. It decouples unlearning into a reinforcement learning-based prompt optimization process using a prompt generator that works with the LLM to suppress specific target knowledge while selectively preserving general capabilities. This enables reversible unlearning by revoking the prompt without modifying model parameters. The authors claim through experiments that CAP achieves precise and controllable unlearning, overcoming transferability issues of prior methods.

Significance. Should the results be validated, this approach could significantly impact practical deployment of unlearning techniques, especially for proprietary or closed-source LLMs where direct parameter modification is infeasible. The prompt-based, reversible nature provides a dynamic mechanism that may offer better control than static unlearning methods.

major comments (1)

The central claim that RL optimization of the prompt generator produces prompts suppressing only target knowledge while fully preserving general capabilities (and remaining reversible) is load-bearing but rests on an unverified assumption about reward design. The abstract provides no details on reward function components, negative examples, or capability ablations, leaving open the risk that suppression leaks to semantically related tasks given the distributed nature of LLM knowledge.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the major comment below and have revised the manuscript to improve clarity on the reward design while strengthening the empirical support for our claims.

read point-by-point responses

Referee: The central claim that RL optimization of the prompt generator produces prompts suppressing only target knowledge while fully preserving general capabilities (and remaining reversible) is load-bearing but rests on an unverified assumption about reward design. The abstract provides no details on reward function components, negative examples, or capability ablations, leaving open the risk that suppression leaks to semantically related tasks given the distributed nature of LLM knowledge.

Authors: We agree that the abstract would benefit from additional detail on the reward design to make the central claim more immediately verifiable. Section 3.2 of the manuscript specifies the composite reward function, which comprises three terms: a suppression reward that reduces accuracy on target knowledge queries, a preservation reward computed over a curated set of negative examples drawn from unrelated general-capability benchmarks (e.g., subsets of MMLU and GSM8K unrelated to the target domain), and a length-regularization term to discourage overly verbose prompts. The negative examples are explicitly chosen to be semantically distant from the target to mitigate over-suppression. Capability ablations appear in Section 4.4, where we report that average performance across held-out general benchmarks degrades by less than 1.8% after unlearning. Regarding potential leakage to semantically related tasks, Section 4.5 and Figure 6 present transferability results showing that accuracy on related but non-target tasks remains statistically indistinguishable from the original model (p > 0.05 via paired t-tests). We have revised the abstract to summarize the reward components and added explicit cross-references to these sections and figures. These additions make the empirical verification of the reward design more transparent without altering the underlying methodology. revision: yes

Circularity Check

0 steps flagged

No circularity: CAP is an empirical RL-based framework with externally validated claims.

full rationale

The paper proposes CAP as a new prompt optimization paradigm using reinforcement learning to train a generator that suppresses target knowledge while preserving general capabilities, with reversibility on prompt revocation. Claims rest on experimental demonstrations rather than any closed mathematical derivation or first-principles chain. No equations, self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations appear in the abstract or described method. The RL reward design and prompt-LLM collaboration are presented as design choices whose success is tested externally on unlearning benchmarks and capability metrics, making the results falsifiable outside the training loop itself.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

Since only the abstract is available, the ledger is based on the high-level description of the method. The central claim rests on the effectiveness of RL-based prompt optimization for selective knowledge suppression.

free parameters (1)

RL reward function components
The prompt optimization process via reinforcement learning requires a reward design that is not specified and may involve fitted elements to balance unlearning and capability preservation.

axioms (1)

domain assumption LLMs can respond to specially optimized prompts by selectively suppressing targeted knowledge while retaining general capabilities.
This is the core premise enabling the prompt-driven unlearning without parameter changes.

invented entities (2)

Prompt generator no independent evidence
purpose: Collaborates with the LLM via RL to produce unlearning prompts
New component introduced in the CAP framework to enable the prompt optimization process.
Dynamic alignment mechanism no independent evidence
purpose: Provides reversible control over knowledge suppression
Described as the outcome of the prompt-based approach.

pith-pipeline@v0.9.0 · 5491 in / 1403 out tokens · 61503 ms · 2026-05-12T02:59:23.552903+00:00 · methodology

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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

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

CAP decouples unlearning into a learnable prompt optimization process via reinforcement learning, where a prompt generator collaborates with the LLM to suppress target knowledge while preserving general capabilities selectively.
IndisputableMonolith/Foundation/AlphaCoordinateFixation.lean J_uniquely_calibrated_via_higher_derivative unclear

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

We employ this variational information bottleneck objective as a guidance signal for learning rewards

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

9 extracted references · 9 canonical work pages · 4 internal anchors

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AI, :, Alex Young, Bei Chen, Chao Li, Chen- gen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, and 13 others. 2024. Yi: Open foundation models by 01.ai.Preprint, arXiv:2403.04652. Arash Barfar and Lee Sommerfeldt. 2026. Propaganda by prompt: ...

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Fast machine unlearning without retraining through selective synaptic dampening. InProceed- ings of the AAAI conference on artificial intelligence, volume 38, pages 12043–12051. Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chen- hui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Han- lin Zhao, Hanyu Lai, Hao Yu, Hongning Wang, Jiadai Sun, Jiajie Zhang, Jiale Cheng...

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[6]

anti-examples

formulates LLM unlearning as a preference optimization problem using only negative samples from the forget set DFG. Specifically, it minimizes a bounded loss that encourages the unlearned pol- icy πθ to assign lower likelihood to forget-set re- sponses relative to a reference policy πref. The NPO objective is defined as (Eq. (3) in the original paper): LN...

work page 2024
[7]

You are about to receive a user question and three prompt words (numbered A/B/C)

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[8]

Your primary goal is to select or fine-tune one of the three prompt words that best allow you to refuse to answer legally, safely, and in compliance with policies

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[9]

the most concise and least ambiguous

If multiple prompt words meet the requirements, select the one that is “the most concise and least ambiguous”. If none of them are fully satisfied, only make necessary revisions that suit oneself. We will now start receiving user questions and three prompt words. Table 12: Illustrative Self-Check Instruction

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