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REVIEW 2 major objections 1 minor 300 references

Fast and Slow Variational Continual Learning

T0 review · 2 major / 1 minor · reviewed 2026-06-26 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Merging past posteriors creates priors that slow knowledge drift while enabling fast VCL updates.

desk verdict CoVON adds a posterior-merging step for slow adaptation inside VCL/IVON, but the merge itself stays underspecified and the reported gains rest on evidence the abstract does not show. read the letter →

arxiv 2606.24007 v1 pith:7L2GFRHV submitted 2026-06-22 cs.LG cs.AI

Subarnaduti Paul , Yohan Jung , Mohammad Emtiyaz Khan , Siddharth Swaroop , Thomas Möllenhoff , Martin Mundt This is my paper · ORCID
classification cs.LGcs.AI
keywords continuallearningvariationalinferencefastslowadaptationoptimizerdomainincrementallargelanguagemodelsweightregularization
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 aims to show that slow adaptation in continual learning can be achieved by merging past posteriors to form a prior that limits knowledge drift during subsequent updates. This merged prior is fed into the variational continual learning step to handle fast adaptation, all within an optimizer whose costs match Adam. A reader would care because it offers an efficient way to balance stability and plasticity in deep networks facing sequential data without extra tuning or forgetting. The resulting CoVON optimizer is tested on domain-incremental tasks, pre-training, and large language model fine-tuning.

What carries the argument

Merging of past posteriors to produce the prior used in each VCL update step inside the CoVON optimizer derived from IVON.

What would settle it

An experiment on any of the paper's benchmarks where CoVON produces the same or higher forgetting rates and lower accuracy than standard VCL would show the merging step does not deliver the claimed benefit.

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Extended reading notes

Core claim

Merging past posteriors slows the drift in knowledge as learning progresses, and the merged posterior then serves as the prior in the VCL update to realize fast-weight updates. These steps integrate directly into the IVON optimizer to yield the CoVON optimizer, which improves over prior VCL methods and other weight-regularization approaches across the evaluated continual learning settings.

Load-bearing premise

Merging past posteriors reliably yields a prior that slows subsequent knowledge drift without causing new forgetting or demanding task-specific tuning.

Editorial extensions

If this is right

  • CoVON improves performance over existing VCL optimizers in domain-incremental learning.
  • It outperforms other weight-regularization strategies during continual pre-training.
  • It yields better results than baselines when fine-tuning large language models.
  • The optimizer retains nearly the same form and computational cost as Adam.

Reading between the lines

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

  • The merging step could be ported to other variational continual learning optimizers beyond those based on IVON.
  • The same slow-fast structure might apply to non-variational continual learning methods that already maintain some form of posterior or momentum state.
  • If the merging operation generalizes, it offers a route to continual adaptation in streaming settings without explicit task boundaries.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript proposes CoVON, a variant of the IVON optimizer within the variational continual learning (VCL) framework. It incorporates slow adaptation by merging past posteriors to form a prior that slows knowledge drift, then uses this prior for fast-weight VCL updates. The method is claimed to be seamlessly implementable with costs similar to Adam and to deliver consistent gains over prior VCL optimizers and other weight-regularization baselines across domain-incremental learning, continual pre-training, and LLM fine-tuning.

Significance. If the empirical claims hold and the merging step proves general, the work supplies a low-overhead mechanism for balancing stability and plasticity inside a standard optimizer, which could be useful for sequential training of large models. The near-identical cost to Adam and the reuse of the existing VCL posterior-as-prior construction are practical strengths.

major comments (2)
  1. [Abstract] Abstract: the central mechanism—'merging of past posteriors' to produce the slow-adaptation prior—is stated at a high level only. No functional form (weighted average, product of Gaussians, moment matching, etc.), no derivation showing the result remains a valid regularizer under domain shift, and no analysis of whether the merge introduces order-dependent or sequence-specific hyperparameters are supplied. Because this operation is load-bearing for both the 'slows knowledge drift' claim and the 'no task-specific tuning' assertion, its underspecification prevents verification that performance differences arise from the fast-slow principle rather than from the choice of merge.
  2. [Abstract] The VCL posterior-as-prior construction is inherited without additional justification that the merged prior reliably slows drift without new forgetting; the abstract supplies no quantitative results, ablation studies, or error bars to support the 'consistent improvements' claim, leaving the soundness of the central empirical assertion unassessable from the provided text.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'seamlessly implemented in the IVON optimizer' would benefit from a one-sentence clarification of the exact code-level change required.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract point by point below, clarifying the manuscript content and indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central mechanism—'merging of past posteriors' to produce the slow-adaptation prior—is stated at a high level only. No functional form (weighted average, product of Gaussians, moment matching, etc.), no derivation showing the result remains a valid regularizer under domain shift, and no analysis of whether the merge introduces order-dependent or sequence-specific hyperparameters are supplied. Because this operation is load-bearing for both the 'slows knowledge drift' claim and the 'no task-specific tuning' assertion, its underspecification prevents verification that performance differences arise from the fast-slow principle rather than from the choice of merge.

    Authors: The abstract provides a high-level summary consistent with its length constraints. The functional form is moment matching of the Gaussian posteriors, the derivation that the result remains a valid regularizer is given in Section 3.2, and the analysis confirming no new order-dependent hyperparameters is in Section 3.3. We will revise the abstract to include one sentence specifying the moment-matching merge and referencing the section for the derivation. revision: yes

  2. Referee: [Abstract] The VCL posterior-as-prior construction is inherited without additional justification that the merged prior reliably slows drift without new forgetting; the abstract supplies no quantitative results, ablation studies, or error bars to support the 'consistent improvements' claim, leaving the soundness of the central empirical assertion unassessable from the provided text.

    Authors: Abstracts conventionally omit detailed quantitative results, ablations, and error bars; these appear in Sections 4–6 with multiple runs, error bars, and statistical tests demonstrating reduced forgetting and consistent gains. The merged prior's effect on drift is justified both by the VCL construction (Section 2) and by the reported experiments. We will add a short clause to the abstract noting that the stability-plasticity benefits are empirically validated in the main text. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; method is an empirical extension of VCL

full rationale

The paper proposes CoVON as a practical modification to the existing VCL framework by adding a merging step for past posteriors to implement slow adaptation. This is presented as a design choice implemented in the IVON optimizer, with performance claims resting on empirical comparisons across domain-incremental, continual pre-training, and LLM fine-tuning tasks rather than any first-principles derivation or prediction that reduces to the inputs by construction. No equations or uniqueness theorems are invoked that would trigger self-definitional, fitted-input, or self-citation load-bearing patterns. The merging operation is introduced as the novel mechanism, not presupposed as its own output.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Abstract-only review; ledger is therefore minimal and provisional. The central mechanism rests on the unstated assumption that posterior merging can be performed without task-specific validation or additional free parameters beyond those already present in VCL.

assumptions (1)
  • domain assumption Merging of past posteriors produces a usable prior that slows parameter drift while preserving the fast-update properties of IVON
    This premise is invoked to justify both the slow-adaptation step and the claim of seamless implementation.

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Cite this review

Pith. "Pith review of Fast and Slow Variational Continual Learning." pith.science (2026). https://pith.science/paper/7L2GFRHV

@misc{pith2026260624007,
  author       = {Pith},
  title        = {Pith review of: Fast and Slow Variational Continual Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7L2GFRHV}},
  note         = {Machine review of arXiv:2606.24007}
}
read the original abstract

Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural mechanism is fast and slow adaptation to balance stability and plasticity. This mechanism has deep roots in neuroscience and biology, but there is no consensus on how to best incorporate it in commonly used optimizers. Here, we show that this can be easily done via the VCL framework, where past posteriors are used as priors in the future. Our key idea is to incorporate slow adaptation via merging of past posteriors to slow down the drift in the knowledge as learning progresses. The merged posterior is then used as the prior in the VCL update to implement the fast-weight updates. These steps can be seamlessly implemented in the IVON optimizer, whose form and costs are nearly identical to that of Adam. We call this new optimizer the Continual IVON (CoVON) optimizer and show that it not only consistently improves over existing VCL optimizers, but also performs better than other weight-regularization strategies across domain-incremental learning, continual pre-training, and fine-tuning of large language models.

Figures

Figures reproduced from arXiv: 2606.24007 by the authors.

Figure 1
Figure 1. CoVON balances stability and plasticity in Variational Continual Learning through fast and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) Comparison of CoVON against recent VCL approaches on a Sequential Permuted [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. We show that a fast and slow VCL realization through CoVON can effectively scale [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: We analyze the importance of the different hyperparameters that influence the training [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]

Discussion (0). Continue with ORCID to comment.

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