Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models

Abdullah X

REVIEW 4 major objections 4 minor 1 cited by

Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper claims that the right to be forgotten for LLMs can be implemented exactly: under deterministic training, a filtered replay of the logged tail reproduces the model trained only on retained data, bit for bit.

desk verdict The exact-unlearning replay idea is real and worth a look, but the byte-identical claim hides a nontrivial data-stream condition that the abstract doesn't name. read the letter →

arxiv 2508.12220 v1 pith:MNAOAHSY submitted 2025-08-17 cs.LG cs.AIcs.CR

Abdullah X This is my paper

classification cs.LGcs.AIcs.CR

keywords righttobeforgottenmachineunlearningdeterministicreplaylargelanguagemodelsauditabilityGDPRbit-identicaltrainingoptimizerstate

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper treats LLM unlearning as a reproducibility problem rather than an approximation problem. It proposes logging a minimal per-microbatch record during training — ordered ID hash, RNG seed, learning-rate value, optimizer-step counter, and accumulation boundary — and, when a forget request arrives, replaying the training tail while filtering only the examples in the forget closure. The central claim is that, when the original run was fully deterministic, this filtered replay produces exactly the same parameters (bit-identical in the training dtype) as training on the retain set alone. The paper also provides complementary approximate paths for latency-constrained settings and reports a controlled demonstration of byte-identical equality of model and optimizer states.

What carries the argument

The load-bearing object is the per-microbatch execution record: ordered ID hash, RNG seed, learning-rate value, optimizer-step counter, and accumulation boundary. This record lets the training run be treated as a deterministic program whose state can be restored at any point, so the forget filter can be applied during replay and the resulting parameters provably equal the retain-only training run. The complementary mechanisms (micro-checkpoints, per-step deltas, adapter deletion, curvature-guided anti-update) are fallback paths for latency or availability constraints, not the exactness mechanism.

What would settle it

Run the same training script twice on identical software but different GPU hardware (or with a nondeterministic op such as atomicAdd enabled); if the resulting model parameters differ by even one bit, the deterministic precondition on which the exact-replay claim rests is violated, so bit-identical equality cannot be reproduced.

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Extended reading notes

Core claim

The core discovery is an exact-replay identity: deleting a set of examples from a model can be achieved not by retraining from scratch but by re-executing the recorded training program from the last checkpoint, skipping every microbatch whose ordered ID hash lies in the forget closure. Re-execution uses the logged RNG seed, learning-rate value, optimizer-step counter, and accumulation boundary per microbatch, so every floating-point operation follows the same order and the optimizer state evolves identically. The paper asserts that under the preconditions (pinned stack, deterministic kernels) the final parameters and optimizer state are bit-identical to a model trained solely on retained dat

Load-bearing premise

The entire training run must be bit-reproducible from the logged per-microbatch records — if any kernel, reduction order, or hardware state introduces nondeterminism, the bit-identical guarantee collapses and the method falls back to approximation.

Editorial extensions

If this is right

If the claim holds, an LLM provider can honor an erasure request with a mathematical guarantee of equality to a retained-only model, not a heuristic approximation.
The audit trail is the existing training log: a compliance auditor can re-run the filtered replay and compare hashes of model and optimizer states.
Storage and latency costs scale with the length of the training tail that must be replayed, bounded by checkpoint interval and micro-checkpoint density.
The exact path requires a fully deterministic training stack, a real engineering constraint but increasingly available with pinned kernels and controlled reductions.
The approximate paths provide graceful degradation when exact replay is infeasible, but they lose the bit-identical guarantee.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same deterministic-logging mechanism could double as a general training provenance system, making any post-hoc data removal — legal or not — a matter of re-executing a filtered trace.
Bit-identity could serve as a formal specification for 'unlearning certification' in future regulation: if a provider demonstrates byte equality with a retained-only checkpoint, erasure is verifiable rather than trust-based.
A natural testable extension is to relax the determinism precondition: if nondeterministic ops are confined to a small part of the graph, one could compute error bounds and route only those layers through the approximate path, yielding a hybrid exactness guarantee.
Combining exact replay with differential privacy would give defense in depth: replay supplies legal erasure while DP bounds training-time memorization leakage.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the replay-with-minimal-log idea is a genuinely useful framing for exact unlearning, and the toy validation is more than most systems papers bother with. But the abstract's 'byte-identical' promise leans on a precondition that is easy to miss: the data pipeline must be invariant under deletion. Standard shuffling doesn't do that. Filtering the forget set out of an already-shuffled stream changes the batch composition, so the replay trajectory diverges from a true retain-set run unless the loader is specially constructed. That's not a small detail. It's the difference between an exact guarantee and an approximate one.

What the paper does well: it treats unlearning as a reproducibility problem, logs a minimal per-microbatch record, and gives multiple fallback paths (rollback, adapter deletion, anti-update) with audit escalation. That's a sensible systems architecture. The abstract is clear about the deterministic-kernel precondition, and the controlled run is a good first check.

Where it's soft: the 'preconditions' are under-specified. If the full text doesn't explicitly require a deletion-invariant data order (e.g., fixed global order with variable-size batches, and a fixed step count), then the central equality claim is misleading. Even with perfect determinism, the filtered replay is not the same as training on the retain set unless that commutation holds. I'd want the authors to state this upfront and prove or at least demonstrate it in the toy run. I also note that 'training tail' is ambiguous—does the replay start from the beginning or from a checkpoint? If from a checkpoint, the earlier steps already saw the forget data, so the final state is not equivalent to a fresh retain-only run. That's another potential gap.

Verdict: this deserves a serious referee, but the referee should push hard on the data-pipeline condition and what 'tail' means. If the full text handles those, it's a solid systems contribution. If not, it's an overclaim.

Referee Report

4 major / 4 minor

Summary. The paper proposes framing LLM unlearning as a reproducible systems problem. It claims that, under a pinned stack and deterministic kernels, replaying the original training tail while filtering the forget closure yields parameters bit-identical to training on the retain set, provided certain preconditions hold. The abstract reports a minimal per-microbatch logging scheme (ID hash, RNG seed, learning-rate value, optimizer-step counter, accumulation boundary) and a controlled run demonstrating byte-identical equality, alongside complementary approximate paths (micro-checkpoints, adapter deletion, curvature-guided anti-update). The paper is presented as an abstract-only submission for review.

Significance. If the central equality claim holds with realistic preconditions, the paper would offer a valuable audit-friendly mechanism for the right to be forgotten in deterministic LLM training pipelines. The complementary approximate paths are sensible practical contributions. However, the significance is currently tempered because the equality claim is essentially a design guarantee that must be carefully scoped; as stated in the abstract, the preconditions are underspecified, and the evidence is limited to a toy artifact.

major comments (4)

[Abstract] The equality theorem implicitly requires a commutation property between dataset deletion and the streaming/shuffling pipeline: Filter(Stream(D, seed), F) must equal Stream(D\F, seed). Standard RNG-shuffled data loaders do not satisfy this, because deleting examples changes the dataset length and therefore the permutation and batch composition. If the preconditions assume a specially constructed pipeline (e.g., fixed global order, variable-size batches, fixed optimizer steps), that assumption must be stated explicitly; otherwise the abstract's promise is misleading. This is load-bearing for the central claim.
[Abstract] The per-microbatch log (ID hash, RNG seed, LR, step counter, accumulation boundary) is sufficient to replay the original run, but the retain-set counterfactual run used a different data stream from the start. The paper must explain how the logged records reconstruct the filtered stream, including how batch boundaries and optimizer steps are adjusted after deletion. Without a precise algorithm and formal derivation, the equality claim is not substantiated.
[Abstract] The claim of 'byte-identical in the training dtype' across a full training run is very strong. Even with deterministic kernels, non-associative floating-point reductions, hardware variations, or multi-device communication order can break exact reproducibility. The paper should define the exact reproducibility stack (e.g., single GPU, fixed CUDA algorithm, no TF32) and provide a formal statement or at least an explicit list of all preconditions. If the preconditions are simply assumed, the claim is unfalsifiable from the abstract.
[Abstract] The empirical validation is limited to 'a toy artifact' and one 'controlled run.' For a systems paper, this is insufficient to demonstrate that the approach scales to LLMs or that the storage/latency budgets are realistic. The paper needs a more substantial evaluation, including a reproducible artifact and measurements on a nontrivial model and dataset.

minor comments (4)

[Abstract] The term 'forget closure' is used without definition; it should be formally defined (e.g., the set of training examples whose influence is to be removed, including any dependencies or derivatives).
[Abstract] The phrase 'replaying the training tail' is vague; the paper should specify whether the entire training trajectory or only a suffix is replayed, and how the initial state is obtained.
[Abstract] The abstract mentions complementary paths (i)-(iii) but does not describe how they interact with the exact replay. A figure or algorithmic sketch would improve clarity.
[Abstract] The GDPR reference is appropriate, but the paper should also discuss technical limitations of 'right to be forgotten' beyond model weights (e.g., memorization in prompts, side channels) to set expectations.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the bit-identical equality claim is conditional on deterministic replay and a deletion-commutative data stream, not derived from the target parameters.

full rationale

The paper's central claim is that replaying the training tail while filtering the forget closure yields the same parameters as training on the retain set, under preconditions. This is a conditional correctness statement, not a derivation that reduces to its own inputs. The method does not fit parameters to target data, nor define the retain-set model in terms of the filtered replay. The equality rests on deterministic execution and a data-stream commutation property (the filtered original stream equals the retain-set stream), which are design requirements rather than circular assumptions. The abstract's 'when preconditions hold' explicitly hedges the claim. The skeptic's concern that standard data loaders do not satisfy commutation is an unstated-precondition or correctness risk, not circularity. No self-citations or imported uniqueness theorems appear in the abstract. Therefore no significant circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's guarantee rests on determinism and complete logging of all training steps that affect the state. These are explicit preconditions rather than hidden assumptions, but they are unproven in the abstract and may be hard to satisfy in production LLM training.

assumptions (3)

domain assumption The training pipeline is fully deterministic under a pinned software stack and deterministic kernels, so the logged per-microbatch record (ordered ID hash, RNG seed, LR, optimizer step, accumulation boundary) is sufficient to replay the exact execution.
The central equality claim is conditional on this precondition, stated in the abstract as 'under a pinned stack and deterministic kernels'.
domain assumption The 'forget closure' is defined and can be computed; every training step that touched the forgotten data is identifiable in the log and can be filtered out.
The replay works only if the set of records to remove is known and the remaining sequential order is preserved.
domain assumption The optimization algorithm and model state are fully determined by the sequence of microbatches and the logged hyperparameters, i.e., no external nondeterminism affects the parameter trajectory.
Bit-identical equality of model and optimizer states requires that the retained order and the same logs always produce the same state, which is a reproducibility assumption.

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Cite this review

Pith. "Pith review of Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models." pith.science (2026). https://pith.science/paper/MNAOAHSY

@misc{pith2026250812220,
  author       = {Pith},
  title        = {Pith review of: Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MNAOAHSY}},
  note         = {Machine review of arXiv:2508.12220}
}

read the original abstract

We study the right to be forgotten (GDPR Art. 17) for large language models and frame unlearning as a reproducible systems problem. Our approach treats training as a deterministic program and logs a minimal per-microbatch record (ordered ID hash, RNG seed, learning-rate value, optimizer-step counter, and accumulation boundary). Under a pinned stack and deterministic kernels, replaying the training tail while filtering only the forget closure yields the same parameters as training on the retain set (bit-identical in the training dtype) when preconditions hold. To meet latency and availability constraints, we add complementary paths: (i) exact reverts of recent steps via micro-checkpoints or dense per-step deltas, (ii) cohort-scoped adapter deletion when the base is frozen, and (iii) a curvature-guided anti-update followed by a short retain-tune, audit-gated with escalation to exact replay. We report storage/latency budgets and a toy artifact validating mechanics; in a controlled run that satisfies the preconditions we demonstrate byte-identical equality of model and optimizer states.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Subtract or Replay? Exact Deletion from Language-Model Memory
cs.LG 2026-07 conditional novelty 6.0 of 10

Exact deletion from LLM memory is representation-dependent: support-vector memory admits exact decrement, while delta-rule recurrent memory requires checkpointed rewind-and-replay.

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Reviewed August 5, 2026 · model on record in the stance chip above.