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REVIEW 4 major objections 6 minor 1 cited by

Unveiling and Addressing Pseudo Forgetting in Large Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Performance drops in continually trained LLMs are often instruction-activation failures, not forgotten abilities.

desk verdict Pseudo forgetting is a real and useful framing, but the GCG suffix-recovery experiment is the load-bearing weak spot and needs a non-optimized control before the 90% recovery claim is taken at face value. read the letter →

arxiv 2411.11932 v2 pith:CQFICFSM submitted 2024-11-18 cs.LG cs.AIcs.CL

Huashan Sun , Yizhe Yang , Yinghao Li , Jiawei Li , Yang Gao This is my paper · ORCID
classification cs.LGcs.AIcs.CL
keywords pseudoforgettingcontinuallearninglargelanguagemodelsinstructiondependencecatastrophicreplay-basedmethodsrationaleguidanceGCGsuffix
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

The paper argues that what looks like catastrophic forgetting in continually trained large language models is often "pseudo forgetting": old-task performance drops not because the model has lost the relevant ability, but because the original instruction no longer triggers that ability. To test this, the authors show that a forgetting model can recover old-task accuracy when given a short correct-rationale prefix, and that appending a task-irrelevant token suffix found by automated search restores correct behavior on more than 90% of forgotten examples. Attribution analysis indicates the cause is reduced dependence on instructions during rationale generation, concentrated in shallow layers. Building on this, the paper introduces a Rationale-Guidance Difficulty score and a replay-allocation framework, RGD-R, that gives more replay data to tasks whose instructions are hardest to use; experiments across five model families report improved final accuracy and lower forgetting while keeping forward transfer.

What carries the argument

The load-bearing object is the pseudo-forgetting diagnosis itself, operationalized through three instruments: partial-rationale guidance, where the first 20% of the ground-truth rationale is inserted after the assistant token; GCG, Greedy Coordinate Gradient search, which optimizes a short, task-irrelevant suffix so that the original instruction plus suffix drives the model toward a correct rationale; and attribution scores over self-attention layers, computed with integrated gradients, that quantify instruction-to-rationale dependency. The RGD score, $\mathrm{RGD}(I,R_g,A_g)=\frac{\mathrm{PPL}_{a-f}(R_g\mid I)}{\mathrm{PPL}_{b-f}(R_g)}$, turns the diagnosis into a per-task number used by RGD-R to set replay ratios. The theoretical link is a conditional-probability decomposition in which the probability of activating the correct capability equals the probability of generating the correct rationale given the instruction divided by its probability without the instruction, so RGD approximates activation difficulty.

What would settle it

A decisive check would be to run the recovery experiment with random tokens in place of the GCG-searched suffix and measure whether recovery collapses; if it does not, the suffix's specific guidance is not what restores performance, and a classifier trained on the first 20% of ground-truth rationales that predicts the answer above chance would show the partial rationale leaks task-relevant cues, defeating the capability-retention reading.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that continual instruction tuning does not erase old-task capabilities; it degrades the instruction-to-capability activation path. Evidence comes from two interventions on models after sequential learning: first, feeding the first k tokens of the correct rationale as external guidance lets the model regenerate the rest of the reasoning and recover pre-forgetting accuracy as k grows; second, searching for a semantically meaningless suffix with gradient-based coordinate search, then appending it to the original instruction, lets the model actively produce the correct rationale and answer, with recovery rates above 90% across tasks. Attribution scores computed by integrated gradients on attention show that pseudo-forgetting models rely less on instruction tokens when generating rationales, especially in shallow layers, which the paper identifies as the mechanism. The paper then defines the RGD score as the ratio of the pseudo-forgetting model's perplexity for the ground-truth rationale under the instruction to the pre-forgetting model's perplexity, and uses it to allocate replay data proportionally to each old task's activation difficulty. RGD-R outperforms equal replay allocation on final average performance, forgetting rate, and backward transfer while preserving forward transfer.

Load-bearing premise

The load-bearing premise is that the first 20% of the ground-truth rationale and the searched-for meaningless suffix carry no task-relevant information, so a recovered prediction reflects retained parametric ability rather than leaked cues, yet the paper's own automated check finds about 5% of the short rationales do leak key information.

Editorial extensions

If this is right

  • If pseudo forgetting is real, a drop in old-task accuracy after continual learning should not be read as evidence that the old ability is gone; the model needs a prompt or guidance that re-activates it.
  • Replay-based continual learning can be made more efficient by allocating replay samples to tasks where instruction-to-capability activation is hardest, which is what RGD-R does.
  • Instruction dependence is concentrated in shallow layers, so interventions targeting instruction following in those layers may mitigate forgetting without storing old data.
  • The suffix-recovery result means old-task performance can be elicited without retraining, purely by prompt modification.

Reading between the lines

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

  • Editorial extension: standard continual-learning benchmarks that report only final accuracy may systematically understate retained knowledge, because re-testing old tasks with prompt variants or minimal guidance would likely produce a different forgetting ranking.
  • Editorial extension: the RGD ratio could be combined with parameter-preservation or layer-specific regularization, targeting the layers where instruction dependence drops most, rather than relying on replay alone.
  • Editorial extension: a natural stress test is to run the same suffix-recovery experiment on open-ended generation and non-English tasks, since the paper's evidence comes from English classification tasks.
  • Editorial extension: the fact that arbitrary token suffixes can wake old-task behavior also raises prompt-robustness and controllability questions, as small prompt changes could shift a model between different learned behaviors.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper introduces and empirically argues for 'pseudo forgetting' in continual learning of LLMs: the observed performance drop on previously learned tasks is claimed to stem not from a loss of task capabilities, but from the original instructions failing to activate those intact capabilities. The evidence is threefold: (A1) providing the first k tokens of the ground-truth rationale partially restores task accuracy; (A2) appending per-sample GCG-searched suffixes to the original instructions yields recovery rates above 90% on forgotten samples; and (2.2) attribution analysis shows reduced instruction dependence during rationale generation after continual learning. Based on this diagnosis, the paper proposes the Rationale-Guidance Difficulty (RGD) metric and an RGD-based replay allocation strategy (RGD-R), evaluated on the Long Sequence Benchmark across several LLM families and sizes.

Significance. If the central claim is established, the paper would meaningfully refine the understanding of catastrophic forgetting in LLMs by shifting attention from parametric erasure to instruction-activation failures, with practical consequences for replay-based continual learning. The manuscript has clear strengths: it attacks the problem with multiple complementary analyses, evaluates across five model sizes/families, includes attribution-based mechanistic evidence, and reports a replay-allocation method with code and data availability. However, the strongest direct evidence for pseudo forgetting rests on the GCG suffix-recovery experiment, which currently lacks the controls needed to distinguish retained capability from susceptibility to per-sample adversarial optimization. The theoretical derivation of the RGD metric is also not mathematically sound as written. The phenomenon is plausible and the paper is worth publishing after substantial revision, but the central claim is not yet convincingly supported.

major comments (4)
  1. [Section 2.1, A2 (Eqs. 1-3)] The GCG suffix-recovery experiment conflates retained capability with susceptibility to per-sample adversarial optimization. Because S_i is optimized to minimize the loss of the target T (Eq. 2) and the recovery rate (Eq. 3) is evaluated on the same (I_i, A_i) used in that optimization, the 'Answer' target is close to a tautology: a high success rate is expected whenever GCG can find any token string that steers the model, regardless of whether the old-task capability is intact. The partial-rationale targets inherit this concern unless it is verified that neither the first 20% of the rationale nor the optimized suffix carries task-relevant information. The manuscript provides no control with a random or fixed non-optimized suffix, no wrong-target control (e.g., a suffix optimized to force an incorrect answer), no transfer test of suffixes across samples of the same task, and no automated check of suffix semantics. The suffixes shown in Table 10 already contain meaningful English fragments such as 'Sounds striking' and 'phony provisions'. These controls are needed before the 90% recovery rate can be read as evidence for retained parametric capability.
  2. [Appendix C.1, Table 9] The only leakage check is a GPT4o judgment on the partial rationale, not on the suffix and not on the combined prompt [I, S_i]. The criterion 'directly leads to the correct answer' is weaker than 'contains no task-relevant information', and Table 9 reports that at k=0.2, 3-6% of partial rationales do leak key information. The paper's statement in Appendix C.1 that it uses the k value that 'does not leak any information' is therefore inaccurate. This matters because the A2 experiment's load-bearing premise is that recovered predictions reflect parametric capability rather than leaked cues.
  3. [Section 3.2, Eqs. (7)-(12)] The theoretical derivation of the RGD metric is not mathematically sound. Eq. (9) writes Pθ(c∗|i)=p(c1,...,cm|i)=Σ_m p(cm|i), conflating the joint probability over capabilities with a marginal sum; Eq. (10) similarly writes Pθ(r∗)=p(r∗|c1,...,cn)=Σ_n p(r∗|cn). Eq. (11) does not follow from the independence assumption in Eq. (8): setting cross terms p(r|cn)p(cm|i)=0 for m≠n does not imply the factorization Pθ(r∗|i)=(Σ_n p(r∗|cn))(Σ_m p(cm|i)). Because this proof is presented as the justification for the RGD score, it should be corrected or replaced by a direct empirical validation of RGD as a heuristic.
  4. [Section 3.1, Eq. (5)] The RGD score divides PPL_{a-f}(Rg|I) by PPL_{b-f}(Rg), where the denominator is not conditioned on the instruction I. To measure instruction-activation difficulty, the natural baseline is PPL_{b-f}(Rg|I) or a matched control; using the unconditional probability means the ratio can change merely because of domain-shift effects on token probabilities, independent of any instruction-activation failure. This weakens the construct validity of the metric, although the empirical replay results could still be useful if the metric is treated as a heuristic.
minor comments (6)
  1. [Section 2.1, Results and Analysis] There is a typo: 'the model dose not forget' should be 'the model does not forget'.
  2. [Figure 5 caption] The phrase '0.2 portions' is unclear; it should say 'the first 20% of the rationale'.
  3. [Section 3.3, Eq. (13)] The summation notation 'Pi−1 k=1' in the text is a typo; it should be a summation symbol with limits k=1 to i−1.
  4. [Table 10] Several displayed suffixes are not obviously 'semantically meaningless' (e.g., 'Sounds striking', 'phony provisions'); a quantitative check of semantic content or a human study would strengthen the claim that the suffixes carry no task-relevant information.
  5. [Tables 1 and 3] The main results report single runs without standard deviations or significance tests. Given the small differences between RGD-R and EA (e.g., Qwen2-0.5B FAP 65.99 vs 64.13; Mistral-7B FAP 74.91 vs 72.15), repeated seeds are needed to establish the stability of the improvement.
  6. [Section 1] The code link is given as 'available at here' without an actual URL; a working repository link should be provided.

Circularity Check

1 steps flagged · score 6.0 of 10

The GCG suffix-recovery experiment (Section 2.1, A2) reports the optimization objective itself as evidence of retained capability; the Answer-target recovery is tautological.

  1. fitted input called prediction [Section 2.1, A2 (GCG suffix-recovery experiment), Eqs. (2)-(3), Table 8; Appendix B.3]
    "For GCG, as shown in Table 8, we explore three optimization targets T : (1) Answer guidance; (2) Partial ground truth rationale guidance; (3) Partial pre-forgetting rationale guidance... L(S) = − log p(T | [I, S]) (2)... R.Ra = 1/|Df| Σ I(Ma−f ([Ii, Si]), Ai) (3)"

    For the Answer-guidance condition, the per-sample optimization target T in Eq. (2) is, per Table 8, 'The answer is: {ground truth answer} . The reasons are as follows:'. The suffix S_i is searched to minimize the loss of generating that exact ground-truth answer, and Eq. (3) then counts whether M_{a-f}([I_i, S_i]) outputs A_i. The evaluation event is the optimization objective itself, so a high recovery rate under this target is the success rate of the GCG fit rather than an independent measurement of a retained capability.

full rationale

The circularity is concentrated in the A2 evidence for pseudo forgetting. In the Answer-target condition, Eq. (2) minimizes the loss of generating the ground-truth answer conditioned on [instruction, suffix], and Eq. (3) measures exactly whether that answer is produced; the 90%+ recovery rate is therefore the GCG fit success rate by construction. The partial-rationale conditions are less tautological, since the target is a 20% rationale prefix rather than the answer, but the suffix is still per-sample optimized to produce that prefix, and the paper's only safeguard is a self-reported GPT4o check of the rationale rather than of the suffix. The A1 partial-rationale experiment is explicitly hedged in Appendix C.1 and cannot by itself prove the claim. The attribution analysis (Section 2.2) and the RGD-R benchmark comparisons have independent content and do not reduce to the fitted-suffix experiment. No load-bearing self-citations or imported uniqueness theorems were found. Because the central claim of retained capability rests on an experiment whose primary condition reports the optimization target as evidence, the score is 6: partial circularity, with one prediction reducing by construction.

Assumptions & free parameters 2 free parameters · 3 assumptions · 1 invented entities

The central claim relies on two assumptions about information content (k=0.2 rationale does not leak, GCG suffix is meaningless) and on a strong independence axiom in the theoretical section. The only fitted quantities that matter are the per-sample GCG suffixes, which make the recovery evidence partially self-fulfilling; no free parameters are fit to the final benchmark numbers. The pseudo-forgetting concept is an invented label with a falsifiable handle.

free parameters (2)
  • Rationale prefix ratio k = 0.2 (0.1 in exploratory plots)
    Hand-chosen threshold for the GCG optimization target and partial-rationale guidance; chosen to limit key-information leakage, verified by GPT4o rather than by a formal control.
  • Per-sample GCG suffix S_i = Token sequences optimized per instance (e.g., '! ! ! ! ! ...')
    Fitted to minimize loss of generating the target rationale (Eq. 2); recovery rate (Eq. 3) is measured with these fitted suffixes, so it conflates search power with retained capability.
assumptions (3)
  • ad hoc to paper Independence of task abilities: for m != n, p(r|cn) * p(cm|i) = 0 (Eq. 8 in Section 3.2)
    Introduced to derive the RGD formula. Real tasks share features and instructions, so the assumption is likely false and is never tested.
  • domain assumption Instruction following can be modeled as Pθ(r|i)=Σ_n p(r|cn)p(cn|i) with discrete latent capabilities (Eq. 7)
    Borrowed from Wu et al. (2024) as a mechanistic model; the paper treats it as given rather than validating it on the studied LLMs.
  • domain assumption The first 20% of a ground-truth rationale (and the GCG suffix) carries no task-relevant key information (Appendix C.1, Table 9)
    Checked only by GPT4o judgments; about 5% of tested rationales still leak key information at k=0.2, and no independent verification of semantic inertness of optimized suffixes is provided.
invented entities (1)
  • Pseudo forgetting independent evidence
    purpose: Label for the hypothesized state where old-task performance drops because instructions fail to activate intact capabilities.
    The paper provides falsifiable handles: prompt interventions should recover old-task performance, and attribution scores should show lower instruction dependence. The GCG-based handle is weakened by the suffix optimization, but the partial-rationale and attribution results give partial independent support.

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Cite this review

Pith. "Pith review of Unveiling and Addressing Pseudo Forgetting in Large Language Models." pith.science (2026). https://pith.science/paper/CQFICFSM

@misc{pith2026241111932,
  author       = {Pith},
  title        = {Pith review of: Unveiling and Addressing Pseudo Forgetting in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CQFICFSM}},
  note         = {Machine review of arXiv:2411.11932}
}
read the original abstract

Although substantial efforts have been made to mitigate catastrophic forgetting in continual learning, the intrinsic mechanisms are not well understood. In this work, we demonstrate the existence of "pseudo forgetting": the performance degradation on previous tasks is not attributed to a loss of capabilities, but rather to the failure of the instructions to activate the appropriate model abilities. We show that the model's performance on previous tasks can be restored through two simple interventions: (1) providing partial external correct rationale, and (2) appending semantically meaningless suffixes to the original instructions, to guide the generation of correct rationales. Through empirical analysis of the internal mechanisms governing rationale generation, we reveal that models exhibiting pseudo forgetting show reduced instruction dependence during rationale generation, leading to suboptimal activation of their inherent capabilities. Based on this insight, we propose Rationale-Guidance Difficulty based Replay (RGD-R) framework that dynamically allocates replay data based on the model's ability to correctly leverage the intrinsic capabilities. Experimental results demonstrate that RGD-R effectively mitigates pseudo forgetting while maintaining model plasticity.

Figures

Figures reproduced from arXiv: 2411.11932 by the authors.

Figure 1
Figure 1. Pseudo forgetting. 1. The performance degra￾dation on previous tasks stems from instructions failing to properly activate the model’s inherent capabilities rather than genuine forgetting of task-relevant abilities. 2. Performance can be restored through appropriate prompting, demonstrating no actual forgetting occurs. 2023b ; Zhao et al. , 2024), limited studies investi￾gate the intrinsic mechanisms underlying this … view at source ↗
Figure 2
Figure 2. Changes in the model’s task performance after forgetting when the first [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Prompt example with additional the first 10% [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Recovery rate of forgotten tasks. 1. For each task, we sample 100 forgotten instances. 2. The labels ‘Answer’, ‘Before R (0.2)’, and ‘Ground Truth R (0.2)’ denote respectively: the ground truth answer, the first 0.2 portions of the rationale generated by the model befo…
Figure 7
Figure 7. Figure 7: Comparison of relative instruction dependency [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 8
Figure 8. Figure 8: Comparison of relative instruction dependency [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: The prompt used to assess whether the first [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 11
Figure 11. Figure 11: Comparison of relative instruction depen [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]

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Forward citations

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