REVIEW 1 major objections 1 minor 7 references
Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future
T0 review · 1 major / 1 minor · reviewed 2026-06-29 · grok-4.3
Pith's one-line read Diffusion models learn generation by reversing a structured process of information destruction, which may offer more flexibility than hand-crafted withholding methods especially with scarce data.
desk verdict This is a perspective framing diffusion as destroy-then-generate with a tutorial using a new PGM variant, but the flexibility claim stays at assertion level without comparisons. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The destroy-then-generate perspective, in which a structured destruction process withholds information and the model is trained to reverse that process.
What would settle it
Train two generative models on the same small dataset, one using diffusion-style destruction and the other using a typical hand-crafted withholding rule, then compare the quality of samples each produces.
Extended reading notes
Core claim
Diffusion models belong to the family of techniques that withhold information and train models to guess the missing parts; their distinctive strength is that the destruction step itself is taken seriously as a general, flexible strategy for learning generation rather than as an arbitrary hand-crafted choice.
Load-bearing premise
Diffusion's structured way of destroying information is structurally more flexible and advantageous than typical hand-crafted information-withholding techniques.
Editorial extensions
If this is right
- Diffusion-style training may produce stronger generators than hand-crafted withholding when data is limited.
- Direct transfer of reinforcement-learning techniques into diffusion training surfaces subtle exploration problems that need resolution.
- Exploration methods designed specifically for diffusion processes could improve sample efficiency.
- Novel probabilistic graphical models can be used to formalize and teach the destroy-then-generate viewpoint.
Reading between the lines
- The same withholding lens might be used to reinterpret and compare other families of generative models.
- Diffusion-native exploration could address training instabilities that appear when data is scarce.
- Direct empirical comparisons between diffusion destruction and hand-crafted withholding would test the flexibility claim.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript frames diffusion models as part of a family of techniques that withhold information from inputs and train models to recover it. It argues that diffusion's continuous destruction process is structurally more flexible than hand-crafted withholding methods and advantageous in data-scarce regimes. The paper discusses challenges in porting RL methods to diffusion and outlines open directions for diffusion-native exploration. A tutorial section introduces a novel class of probabilistic graphical models to support the destroy-then-generate perspective.
Significance. If the flexibility claim were substantiated with concrete comparisons or examples, the work could offer a unifying conceptual lens on generative modeling and suggest new avenues for low-data training. The tutorial's introduction of a novel PGM type might provide expository value for analyzing information-withholding models. As written, however, the central thesis remains an assertion without supporting derivation, task-specific analysis, or empirical grounding.
major comments (1)
- [Abstract and thesis section] Abstract and thesis section: the claim that diffusion's destroying approach 'is more flexible than typical hand-crafted information withholding techniques, providing a rich training playground' is load-bearing for the paper's main argument yet is advanced without any concrete comparison of withholding mechanisms, without an example task where diffusion enables training impossible under hand-crafted masks, and without analysis of why the continuous noise schedule is richer than discrete or structured alternatives.
minor comments (1)
- [Tutorial] The tutorial's exposition of the novel probabilistic graphical model would benefit from an explicit definition, a diagram, or a comparison to standard PGMs to make the technical contribution clearer.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback highlighting the need for stronger substantiation of the central flexibility claim. The manuscript is a position paper advancing a conceptual perspective on diffusion as a destroy-to-reconstruct strategy, accompanied by a tutorial on novel probabilistic graphical models. We respond to the major comment below, maintaining that the work's value lies in framing open questions rather than exhaustive empirical validation.
read point-by-point responses
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Referee: [Abstract and thesis section] Abstract and thesis section: the claim that diffusion's destroying approach 'is more flexible than typical hand-crafted information withholding techniques, providing a rich training playground' is load-bearing for the paper's main argument yet is advanced without any concrete comparison of withholding mechanisms, without an example task where diffusion enables training impossible under hand-crafted masks, and without analysis of why the continuous noise schedule is richer than discrete or structured alternatives.
Authors: We acknowledge that the flexibility argument is presented at a conceptual level without direct head-to-head comparisons of specific withholding mechanisms or a concrete task example demonstrating diffusion enabling training impossible under discrete masks. The manuscript does not include such derivations or analyses because it is structured as an exploratory thesis outlining a unifying lens and open directions, rather than a technical paper with new experiments. The continuous noise schedule is argued to be richer due to its ability to parameterize a continuum of corruption levels (from near-identity to full noise) in a single training objective, contrasting with fixed hand-crafted masks that require separate designs per corruption type; however, we agree this remains an assertion without formal proof or task-specific breakdown in the current text. No revision is planned to add empirical comparisons, as that would shift the paper's scope beyond its intended conceptual and tutorial contributions. revision: no
Circularity Check
No circularity detected; conceptual thesis with no load-bearing derivations or self-referential reductions
full rationale
The paper advances a conceptual perspective that diffusion's structured destruction provides a richer training signal than hand-crafted withholding, framed as an argument rather than a derived result. No equations, fitted parameters, or self-citations are present in the provided text that would reduce any claim to its own inputs by construction. The introduction of a novel class of probabilistic graphical models is presented as an expository tool, not as a uniqueness theorem or ansatz smuggled from prior work. The discussion of RL porting issues is explicitly left as open questions. This is a self-contained opinion piece whose central claim does not rely on any of the enumerated circular patterns.
Assumptions & free parameters
invented entities (1)
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novel kind of probabilistic graphical model
Cite this review
Pith. "Pith review of Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future." pith.science (2026). https://pith.science/paper/RNMD3WJJ
@misc{pith2026260530553,
author = {Pith},
title = {Pith review of: Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future},
year = {2026},
howpublished = {\url{https://pith.science/paper/RNMD3WJJ}},
note = {Machine review of arXiv:2605.30553}
}
read the original abstract
I present diffusion models as part of a family of machine learning techniques that withhold information from a model's input and train it to guess the withheld information. I argue that diffusion's destroying approach to withholding is more flexible than typical hand-crafted information withholding techniques, providing a rich training playground that could be advantageous in some settings, notably data-scarce ones. I then address subtle issues that may arise when porting reinforcement learning techniques to the diffusion context, and wonder how such exploration problems could be addressed in more diffusion-native ways. I do not have definitive answers, but I do point my fingers in directions I deem interesting. A tutorial follows this thesis, expanding on the destroy-then-generate perspective. A novel kind of probabilistic graphical models is introduced to facilitate the tutorial's exposition.
Reference graph
Works this paper leans on
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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Bansal, Arpit et al. (2023). “Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise”. In:Advances in Neural Information Processing Systems. Ed. by A. Oh et al. Vol
2023
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[2]
Diffusion Schrödinger Bridge with Applications to Score- Based Generative Modeling
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[3]
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Curran Associates, Inc., pp. 17695–17709.url:https: //proceedings.neurips.cc/paper_files/paper/2021/file/940392f5f32a7ade1cc2 01767cf83e31-Paper.pdf. Fathi, Nima, Torsten Scholak, and Pierre-André Noël (2025). “Unifying Autoregressive and Diffusion-Based Sequence Generation”. In:Second Conference on Language Modeling.url: https://openreview.net/forum?id=r...
work page Pith review arXiv 2021
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[4]
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Curran Associates, Inc., pp. 6840–6851.url:https://proceedings.neurips.cc/paper _files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf. Kim, Jaeyeon et al. (2025). “Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions”. In:Forty-second International Conference on Machine Learning.url:https://openreview.net/f...
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Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Curran Associates, Inc., pp. 21696–21707.url:https://proceedings.neurips.cc/paper_files/paper/2 021/file/b578f2a52a0229873fefc2a4b06377fa-Paper.pdf. 33Though you may have to narrate it from the perspective of Laplace’s demon, a narrative device that already knows everything there is to be known. 20 Latham, P. E. and Y. Roudi (2009). “Mutual information”. ...
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[6]
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Curran Associates, Inc., pp. 130136–130184.doi:10.52202/079017-4135. url: https://proceedings.neurips.cc/paper_files/paper/2024/file/eb0b13cc51 5724ab8015bc978fdde0ad-Paper-Conference.pdf. Shao, Zhihong et al. (2024). “Deepseekmath: Pushing the limits of mathematical reasoning in open language models”. In:arXiv preprint arXiv:2402.03300.url:https://arxiv....
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[7]
21 For BibTEX, please use @inproceedings{noel2026destruction, author = {No\"el, Pierre-Andr\’e}, title = {Destruction is a General Strategy to Learn Generation; Diffusion’s Strength is to Take it Seriously; Exploration is the Future}, booktitle = {ICLR Blogposts 2026}, year = {2026}, date = {April 27, 2026}, note = {https://iclr-blogposts.github.io/2026/b...
2026
Reviewed June 29, 2026 · model on record in the stance chip above.
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