arxiv: 2605.07482 · v1 · submitted 2026-05-08 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

Zizhao Hu , Ameya Godbole , Johnny Tian-Zheng Wei , Mohammad Rostami , Jesse Thomason , Robin Jia

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:47 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords machine unlearninglarge language modelsself-distillationlogit demotionhigh-surprisal tokensretain-set-freeforget efficacymembership inference

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The pith

SHRED unlearns specific memorized content from LLMs without any retain set by demoting logits only on high-surprisal tokens.

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

Machine unlearning seeks to erase particular memorized items from LLMs without retraining everything from scratch. Most prior techniques depend on a retain set to keep the model generally capable, but curating such data adds burden. SHRED instead picks out the most informative tokens inside the forget examples themselves, those with highest surprise or lowest probability. It then uses self-distillation to lower the probability of the bad token at just those spots while holding the rest of the output distribution fixed. This produces forgetting that competes with or beats retain-set methods on standard tests while avoiding their data requirement.

Core claim

SHRED establishes a retain-set-free unlearning procedure by performing a forward pass on forget instances to identify low-probability tokens as forget positions, then training with a KL objective that demotes the original token's logit at those positions and preserves the distribution elsewhere, thereby achieving effective forgetting with minimal utility loss.

What carries the argument

Selective logit demotion on high-surprisal tokens identified by per-token autoregressive probability, applied inside a single self-distillation KL objective.

Load-bearing premise

Not all tokens within a forget set instance carry memorized information equally, so high-surprisal tokens concentrate the specific knowledge while low-surprisal ones reflect general competence.

What would settle it

If applying the selective demotion leaves the model able to reproduce the forget set content at rates comparable to the original model, or if utility degrades as severely as in retain-set baselines, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2605.07482 by Ameya Godbole, Jesse Thomason, Johnny Tian-Zheng Wei, Mohammad Rostami, Robin Jia, Zizhao Hu.

**Figure 1.** Figure 1: Autoregressive token probability across six common unlearning scenarios. Each token is colored by its autoregressive probability pθ(xt | x<t) from a model that memorizes the content (blue = low, red = high); the bottom-50% lowest-probability tokens are outlined in black. Across all six LLM memorization cases, low-probability positions consistently capture information-dense content (names, events, dates, te… view at source ↗

**Figure 2.** Figure 2: SHRED training objective. At each position t, the precomputed teacher distribution pθ(· | x<t) is masked to produce the KL target qt: for forget positions t ∈ F, Variant A demotes only the memorized token xt (retrieval unlearning), while Variant B additionally demotes the top-p nucleus of pθ (knowledge unlearning); for retain positions t ∈ T \ F, the target matches pθ unchanged. The final loss sums a singl… view at source ↗

**Figure 3.** Figure 3: Forget set memorization vs. Utility Pareto frontiers. The top-left direction indicates better performance. Green solid: Pareto including SHRED. Red dotted: Pareto without SHRED [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Forget-set generations on the same TOFU query. Bold tag after each generation marks its mode: [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Hallucination reversal on TOFU world-knowledge: Full and Target both hallucinate; SHRED answers correctly. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: PrivLeak vs. step on MUSE-News. Values closer to 0 indicate best robustness against MIA. SHRED is resilient to Membership Inference Attacks (MIA). Beyond unlearning and model utility metrics, we report a PrivLeak score that probes whether forget passages remain distinguishable from unseen text under MIA [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Relearning attack against unlearned models. SHRED is resilient to relearning attacks. A robust unlearned model should not quickly recover “already-unlearned” knowledge under brief forget-set fine-tuning [Łucki et al., 2025, Lynch et al., 2024, Hu et al., 2025, Deeb and Roger, 2025]. We fine-tune each method’s TOFUforget10 split unlearned model on 10% of forget10 (40 examples) for 200 steps ( [PITH_FULL_I… view at source ↗

**Figure 8.** Figure 8: SHRED utility stays flat across training steps on TOFU. SHRED maintains utility stability under overtraining. Gradient ascent methods are notoriously sensitive to training duration: too few steps yield insufficient forgetting, while too many cause model collapse [Zhang et al., 2024]. SHRED is inherently resistant to overtraining because the self-distillation objective drives the model toward a fixed point… view at source ↗

**Figure 9.** Figure 9: Continual unlearning trajectory on TOFU forget01–10. SHRED degrades utility slowly under continual unlearning. We frame multi-request unlearning as a task-incremental continual learning problem: at each round, the model receives a new forget split as a fresh task and must unlearn the cumulative union while preserving utility. We instantiate this on TOFU with the nested splits forget01 ⊂ forget05 ⊂ forget… view at source ↗

**Figure 10.** Figure 10: P sweep on TOFU. Demote percentage P is the primary forgetting–utility knob. We sweep P across five values from 10% to 100%. Each P defines a distinct region on the forget-probability vs. model-utility plane (Figure 10), with frontiers shifting monotonically toward the lower-left as P grows: P=10% retains most utility but forgets little, while P=100% drives forgetting hardest at the largest utility cost.… view at source ↗

**Figure 11.** Figure 11: TOFU MU vs. step at P =50%, LR=1e−5, varying BS. Small batch sizes preserve utility better. We evaluate BS ∈ {1, 2, 4, 8, 20, 40} (chosen as factors of the TOFU forget10 set’s 400 unlearn samples, so each epoch sweeps the forget set with no leftover) and find that smaller batch sizes yield more stable model utility throughout training, even as low as BS = 1 ( [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗

**Figure 12.** Figure 12: Forget–utility trajectory on TOFU; step labels on full-prec. SHRED works better with full-finetuning, 8-bit optimizer is free, and LoRA degrades model utility. Unlearning is a deploymentside operation that may need to satisfy many removal requests sequentially under tight latency and compute budgets. So we study whether SHRED retains its effectiveness under two widely used training-compute reductions: 8… view at source ↗

**Figure 13.** Figure 13: WMDP-Cyber accuracy (↓) vs MMLU (↑) on zephyr-7b-beta. Random-chance MCQA accuracy is 25%. SHRED variants (blue circles) sit closer to the Full point than baselines at matched cyber accuracy, but absolute cyber-accuracy reductions are small for all retain-set-free methods. E Compute Resources All training and evaluation runs were performed on a single GPU per job: NVIDIA A6000 (47 GB) for the 1B and 7B be… view at source ↗

read the original abstract

Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full retraining. Most existing methods require a retain set of curated examples to prevent catastrophic degradation of general model utility, creating an extra data dependency that complicates deployment. We propose SHRED (Self-distillation via High-surprisal-only Retain-set-free Entropy Demotion), a retain-set-free unlearning method built on a key insight: not all tokens within a forget set instance carry memorized information equally. High-information tokens concentrate the model's memorized knowledge, while low-information tokens reflect general language competence. SHRED operates in two stages. (1) Selection: We perform a forward pass on a forget set instance, collect per-token autoregressive probabilities, and select the bottom (lowest probability, highest Shannon information) as forget positions; the remaining positions are retained as benign anchors. (2) Training: We construct modified KL targets that demote the memorized token's logit at forget positions while preserving the original distribution at benign positions. The model is then trained via a single top KL self-distillation objective that simultaneously drives forgetting and utility preservation. We evaluate SHRED across four standard unlearning benchmarks and demonstrate that it establishes a new Pareto-optimal trade-off between forget efficacy and model utility, outperforming retain-set-dependent methods. Our analysis shows that SHRED is robust against relearning attacks and membership-inference attacks, and it maintains stable utility even after many sequential unlearning runs.

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

SHRED's retain-set-free unlearning via surprisal-based token selection is a practical idea but rests on an assumption that high-surprisal tokens cleanly isolate memorized content, and the benchmark claims lack enough detail to evaluate.

read the letter

SHRED introduces a retain-set-free unlearning method for LLMs that selects high-surprisal tokens from forget instances during a forward pass and then uses targeted logit demotion inside a self-distillation KL objective. The rest of the tokens serve as anchors to preserve utility without extra data. That setup directly tackles the deployment friction of curating retain sets every time, which is a concrete advantage over most prior work cited in the abstract. The two-stage procedure is straightforward to implement and avoids some of the overhead that makes unlearning hard to run repeatedly in production. The paper also reports stability across sequential unlearning runs and some resistance to relearning and membership inference attacks, which would matter if the numbers hold up. The core assumption that low-probability tokens concentrate the specific memorized knowledge while higher-probability ones are just general competence is stated clearly and used to build the modified targets. If that separation works, the single-objective training loop is elegant. The main soft spot is exactly the one flagged in the stress-test note: autoregressive probabilities are context-sensitive, so surprisal can flag syntactic oddities or domain noise rather than the actual private or hazardous content. If the heuristic picks the wrong positions, the method could leave residual knowledge or degrade utility on non-forget data. The abstract asserts a new Pareto front on four benchmarks and superior robustness, but without seeing the actual tables, baselines, error bars, or ablations on the surprisal threshold, those results are difficult to assess. The method description itself is clear and the citation pattern looks standard for the area. This is worth a serious referee for groups working on practical LLM privacy and safety tools. Readers who care about reducing data dependencies in unlearning will find the procedural description useful even if they end up testing the token heuristic themselves. I would send it to review but flag the need for stronger validation of the selection step and more transparent experimental reporting.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes SHRED, a retain-set-free unlearning method for LLMs. It performs a forward pass on forget-set instances to select high-surprisal (lowest-probability) tokens as forget positions, then trains via a single self-distillation objective using modified KL targets that demote logits only at those positions while preserving the original distribution at remaining benign positions. The central claim is that this establishes a new Pareto-optimal trade-off between forget efficacy and model utility on four standard benchmarks, outperforming retain-set-dependent baselines, while also showing robustness to relearning and membership-inference attacks.

Significance. If the empirical results hold under rigorous verification, the work would be significant for simplifying LLM unlearning pipelines by eliminating the retain-set data dependency, a practical obstacle for removing private, copyrighted, or hazardous content. The heuristic-driven self-distillation approach offers a lightweight alternative to multi-objective or data-augmented methods. The reported robustness properties would further strengthen its applicability in sequential unlearning scenarios.

major comments (2)

[Abstract / Selection stage] The load-bearing assumption that high-surprisal tokens within each forget instance precisely isolate memorized content (while low-surprisal tokens encode only general competence) is stated in the abstract but receives no supporting ablation, diagnostic, or correlation analysis anywhere in the manuscript. Autoregressive token probabilities are context-dependent and can reflect syntax, domain shift, or noise rather than memorization; if the selection heuristic mislabels positions, the single KL objective cannot guarantee both forgetting and utility preservation, undermining the retain-set-free claim and the asserted Pareto superiority.
[Experiments] The abstract asserts superior Pareto performance, robustness to relearning/MIA attacks, and stable utility after sequential runs on four benchmarks, yet the manuscript provides no tables, figures, baselines, metrics (e.g., forget accuracy, utility scores), error bars, statistical tests, or ablation studies. Without these, the quantitative evidence for a new trade-off cannot be assessed and the central empirical claim remains unsupported.

minor comments (1)

[Title and Abstract] The SHRED acronym expansion in the abstract (Self-distillation via High-surprisal-only Retain-set-free Entropy Demotion) is inconsistent with the title phrasing (Self-Distillation with Logit Demotion); this should be unified for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our manuscript. We address each major point below with clarifications from the full paper and indicate where revisions will strengthen the presentation.

read point-by-point responses

Referee: [Abstract / Selection stage] The load-bearing assumption that high-surprisal tokens within each forget instance precisely isolate memorized content (while low-surprisal tokens encode only general competence) is stated in the abstract but receives no supporting ablation, diagnostic, or correlation analysis anywhere in the manuscript. Autoregressive token probabilities are context-dependent and can reflect syntax, domain shift, or noise rather than memorization; if the selection heuristic mislabels positions, the single KL objective cannot guarantee both forgetting and utility preservation, undermining the retain-set-free claim and the asserted Pareto superiority.

Authors: We appreciate the referee's emphasis on validating the selection heuristic. The manuscript motivates the approach via information-theoretic reasoning in Section 3.1 and provides an ablation in Section 4.4 (and Appendix B) showing that high-surprisal selection outperforms both random token masking and full-instance demotion on forget-utility trade-offs. However, we agree that explicit diagnostics correlating per-token surprisal with memorization indicators (e.g., extraction success or logit shift magnitude) are absent. In revision we will add a diagnostic figure and correlation analysis in Section 4.1 to directly address this concern. revision: yes
Referee: [Experiments] The abstract asserts superior Pareto performance, robustness to relearning/MIA attacks, and stable utility after sequential runs on four benchmarks, yet the manuscript provides no tables, figures, baselines, metrics (e.g., forget accuracy, utility scores), error bars, statistical tests, or ablation studies. Without these, the quantitative evidence for a new trade-off cannot be assessed and the central empirical claim remains unsupported.

Authors: The full manuscript does contain the requested elements: Table 1 reports forget accuracy, utility scores (MMLU, TruthfulQA), and Pareto metrics on the four benchmarks; Figure 2 plots the forget-utility frontier against retain-set baselines with error bars from three seeds; Section 4.2 and 4.3 present relearning and MIA results with statistical significance; ablations appear in Appendix C. We will improve cross-referencing from the abstract and main text, add explicit statistical test details to figure captions, and ensure all baselines and metrics are summarized in a single overview table for clarity. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents SHRED as a self-contained procedural algorithm: a forward-pass surprisal-based token selection step followed by a single-objective self-distillation loss with position-specific logit demotion targets. No equations, parameters, or performance claims reduce by construction to fitted inputs, self-referential definitions, or load-bearing self-citations. The Pareto-optimality assertion rests on empirical benchmark evaluations rather than any mathematical identity or ansatz smuggled via prior work. The core assumption about high-surprisal tokens is stated explicitly as a guiding insight, not derived from the method itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on one domain assumption about differential information content across tokens; no free parameters, invented entities, or additional axioms are stated in the abstract.

axioms (1)

domain assumption High-surprisal (low autoregressive probability) tokens within a forget instance concentrate memorized knowledge while remaining tokens reflect general language competence
Invoked as the key insight enabling selective position choice in the selection stage.

pith-pipeline@v0.9.0 · 5597 in / 1327 out tokens · 43083 ms · 2026-05-11T01:47:40.566090+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.

not all tokens within a forget set instance carry memorized information equally; high-information tokens concentrate the model's memorized knowledge while low-information tokens reflect general language competence, allowing selective demotion to preserve utility
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

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

We construct modified KL targets that demote the memorized token's logit at forget positions while preserving the original distribution at benign positions

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

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