arxiv: 2604.24952 · v1 · submitted 2026-04-27 · 💻 cs.CV · cs.AI

Recognition: unknown

Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization

Xinxin Liu , Ming Li , Zonglin Lyu , Yuzhang Shang , Chen Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-08 04:13 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords direct preference optimizationsemi-supervised learningdiffusion modelslabel noisepreference alignmentimage generationhuman preferences

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The pith

Compressing multi-dimensional human preferences into binary labels creates conflicting gradients that misguide diffusion DPO, which a semi-supervised method can fix by pseudo-labeling conflicts from a clean subset.

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

Existing preference datasets annotate images with a single win-lose label even though human judgments involve separate dimensions such as aesthetics, detail quality, and semantic fit. This compression produces pairs that send opposing update signals during direct preference optimization of diffusion models. Semi-DPO first isolates the subset of pairs on which multiple models agree, trains an initial model on that clean data, and then uses the model itself to assign pseudo-labels to the remaining conflicting pairs. The process repeats, refining the model without any new human labels or separate reward model.

Core claim

The paper demonstrates that binary compression of multi-dimensional preferences generates conflicting gradient signals during Diffusion Direct Preference Optimization. Semi-DPO counters this by designating consistent pairs as clean labeled data, training a model on the resulting consensus-filtered subset, and then applying that model as an implicit classifier to produce pseudo-labels for the conflicting pairs, followed by iterative refinement.

What carries the argument

The semi-supervised pipeline that extracts a consensus-filtered clean subset to train an initial model which then serves as an implicit classifier to generate pseudo-labels for conflicting preference pairs.

If this is right

Standard DPO training on the full noisy dataset is outperformed by the iterative pseudo-labeling procedure.
No additional human annotations or explicit reward models are required to reach the reported alignment gains.
The method applies directly to existing preference datasets collected for diffusion models.
Iterative refinement on the pseudo-labeled set produces measurable improvements in multi-dimensional preference alignment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same noise pattern from collapsing multiple preference axes into one binary label likely appears in non-diffusion preference optimization settings.
Replacing the implicit classifier with a more expressive model could further reduce residual label errors on the conflicting pairs.
Applying the consensus-filter plus pseudo-label loop to text-based preference data could test whether the conflicting-gradient problem is modality-specific.

Load-bearing premise

That the model trained on the consensus-filtered clean subset can reliably generate accurate pseudo-labels for the conflicting pairs instead of propagating errors from the initial clean data.

What would settle it

Training a second model on the same conflicting pairs but with human-verified labels instead of the model's pseudo-labels and measuring whether Semi-DPO still matches or exceeds that performance would directly test whether the pseudo-label step recovers the intended preference signals.

Figures

Figures reproduced from arXiv: 2604.24952 by Chen Chen, Ming Li, Xinxin Liu, Yuzhang Shang, Zonglin Lyu.

**Figure 1.** Figure 1: Resolving label noise from multi-dimensional preferences. Standard Diffusion-DPO learns from a noisy, conflict signal created when a binary label (A ≻ B) collapses multi-dimensional preferences (e.g., A’s composition vs. B’s texture). Our method, Semi-DPO, resolves this by decoupling the conflict into refined timestep aware signals via self-training, leading to a more robust alignment. an unlabeled set tha… view at source ↗

**Figure 2.** Figure 2: Semi-DPO framework for resolving label noise. Stage 1 ( view at source ↗

**Figure 3.** Figure 3: Sample images generated by different models for various prompts. view at source ↗

**Figure 4.** Figure 4: Correlation between real user preferences and model predictions. The x-axis of each subplot represents the annotated real human preferences, and the y-axis denotes the model’s predictions. We examine three models: CLIP score, PickScore, and MPS. Each subplot is annotated with the calculated correlation coefficient R-value, where a higher R-value indicates a closer alignment of the model’s predictions with … view at source ↗

**Figure 5.** Figure 5: Qualitative comparison of samples from the labeled dataset (Left, Blue) and the unlabeled dataset (Right, Green). The annotations Winner (green) and Loser (red) indicate the human preference labels. The corresponding full prompts are: Top-Left: “Cosmic hamburger in space”; Bottom-Left: “Cat with a sign that says ‘Cat lives matter’ ”; Top-Right: “An empowering view of a orca warrior wearing royal robe, sitt… view at source ↗

**Figure 6.** Figure 6: Qualitative comparison of Semi-DPO against baseline models view at source ↗

read the original abstract

Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are simply marked as winner or loser. We theoretically demonstrate that compressing multi-dimensional preferences into binary labels generates conflicting gradient signals that misguide Diffusion Direct Preference Optimization (DPO). To address this, we propose Semi-DPO, a semi-supervised approach that treats consistent pairs as clean labeled data and conflicting ones as noisy unlabeled data. Our method starts by training on a consensus-filtered clean subset, then uses this model as an implicit classifier to generate pseudo-labels for the noisy set for iterative refinement. Experimental results demonstrate that Semi-DPO achieves state-of-the-art performance and significantly improves alignment with complex human preferences, without requiring additional human annotation or explicit reward models during training. We will release our code and models at: https://github.com/L-CodingSpace/semi-dpo

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

Semi-DPO filters consistent preference pairs for clean training then pseudo-labels the rest, but the accuracy of those labels is unverified and central to the claim.

read the letter

The paper identifies a real issue in diffusion DPO: multi-dimensional human preferences get collapsed into binary win/lose labels, which creates conflicting gradients during optimization. They respond with Semi-DPO, a semi-supervised pipeline that first trains on consensus-filtered clean pairs, then treats the model as an implicit classifier to label the conflicting pairs and iterates. This combination is not standard in the DPO literature and gives a concrete way to use existing noisy data without new annotations or reward models.

Referee Report

2 major / 2 minor

Summary. The paper claims that binary preference labels compress multi-dimensional human visual judgments (aesthetics, fidelity, semantics) into noisy winner/loser annotations, producing conflicting gradients that impair Diffusion DPO. It introduces Semi-DPO, which treats consensus-filtered consistent pairs as clean labeled data, trains an initial model on them, uses that model as an implicit classifier to assign pseudo-labels to conflicting pairs, and performs iterative refinement. Experiments are reported to show state-of-the-art alignment performance without extra human annotations or explicit reward models.

Significance. If the pseudo-labeling step is shown to be accurate and the gradient-conflict analysis is rigorous, the work could meaningfully advance preference optimization for generative vision models by making better use of existing noisy datasets. The semi-supervised framing is a natural extension of standard SSL techniques to DPO and the no-additional-annotation property is practically attractive. Credit is due for identifying the multi-dimensional noise issue and for committing to release code and models.

major comments (2)

[Method and Experiments] The central claim that Semi-DPO successfully mitigates noise via pseudo-labeling rests on the unverified assumption that the model trained solely on the consensus-filtered clean subset generalizes to produce accurate pseudo-labels on conflicting pairs. No ablation on pseudo-label precision (e.g., agreement rate with held-out human labels) or sensitivity to clean-subset size is reported; without these, it remains possible that reported gains derive only from the clean data or from implicit regularization rather than noise handling.
[Introduction / Theoretical Analysis] The abstract states that conflicting gradients are 'theoretically demonstrated,' yet the manuscript provides no explicit derivation, equation, or proof sketch showing how multi-dimensional compression produces opposing gradient directions in the DPO loss. This derivation is load-bearing for motivating the semi-supervised approach over standard DPO.

minor comments (2)

[Abstract] The abstract would benefit from a one-sentence summary of the key theoretical insight (e.g., the form of the gradient conflict) to allow readers to assess novelty without reading the full derivation.
Ensure the promised GitHub release includes the exact data splits, filtering thresholds for the 'consensus' clean set, and reproduction scripts so that the pseudo-labeling procedure can be independently verified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and valuable feedback. We have carefully considered each comment and made revisions to address the concerns raised. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

Referee: [Method and Experiments] The central claim that Semi-DPO successfully mitigates noise via pseudo-labeling rests on the unverified assumption that the model trained solely on the consensus-filtered clean subset generalizes to produce accurate pseudo-labels on conflicting pairs. No ablation on pseudo-label precision (e.g., agreement rate with held-out human labels) or sensitivity to clean-subset size is reported; without these, it remains possible that reported gains derive only from the clean data or from implicit regularization rather than noise handling.

Authors: We agree that direct validation of the pseudo-labeling step strengthens the central claim. While the original experiments emphasized end-to-end alignment gains, we have added two new ablations in the revised manuscript: (1) a precision evaluation that compares generated pseudo-labels against a held-out subset of human annotations on conflicting pairs, and (2) a sensitivity study that varies the fraction of consensus-filtered clean data used for the initial model (reporting performance curves for multiple clean-subset sizes). These results are presented in the new Section 4.4 and Appendix C. The added experiments indicate that pseudo-label accuracy is sufficient to yield gains beyond training on clean data alone and that improvements are robust across clean-subset sizes, supporting that noise mitigation is a contributing factor. revision: yes
Referee: [Introduction / Theoretical Analysis] The abstract states that conflicting gradients are 'theoretically demonstrated,' yet the manuscript provides no explicit derivation, equation, or proof sketch showing how multi-dimensional compression produces opposing gradient directions in the DPO loss. This derivation is load-bearing for motivating the semi-supervised approach over standard DPO.

Authors: We appreciate the referee highlighting this gap in presentation. Although the motivation section discussed the multi-dimensional nature of preferences, we did not supply a formal derivation. In the revised version we have inserted a new subsection (Section 3.1) that provides an explicit derivation: we model each preference as a vector in a multi-dimensional space, show that the binary label corresponds to a lossy projection, and derive the resulting gradient terms in the DPO objective to demonstrate that opposing directions can arise across dimensions. A concise proof sketch is included. This addition directly supports the motivation for treating conflicting pairs via semi-supervised pseudo-labeling. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper applies standard semi-supervised learning: consensus filtering to create a clean subset, initial training on that subset, and use of the resulting model to pseudo-label conflicting pairs before refinement. No equation or step reduces the claimed performance gain to a quantity defined by the method's own outputs or to a self-citation chain. The gradient-conflict analysis is presented as an independent theoretical observation rather than a tautological restatement of the algorithm's inputs. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the method introduces no explicit free parameters or new invented entities. It rests on standard domain assumptions from semi-supervised learning and DPO.

axioms (1)

domain assumption A model trained on consensus-filtered pairs can serve as a reliable implicit classifier for generating pseudo-labels on conflicting pairs.
This assumption underpins the iterative refinement step described in the abstract.

pith-pipeline@v0.9.0 · 5485 in / 1132 out tokens · 32100 ms · 2026-05-08T04:13:25.226474+00:00 · methodology

discussion (0)

Reference graph

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6.3 THEUSE OFLARGELANGUAGEMODELS Large Language Models (LLMs) such as GPT were used solely for language polishing and clarity improvements in the writing of this paper

Thus, the total variance has a lower bound determined by the conflict: Var[ξt]≥p a,k ·p c,k ·(m (t) a,k +m (t) c,k)2 (16) This proves that any non-zero conflict mass (pc,k >0) introduces a variance term that grows quadrat- ically with the sum of the conflicting and aligned update magnitudes. 6.3 THEUSE OFLARGELANGUAGEMODELS Large Language Models (LLMs) su...

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better” and “worse

or generating new datasets through multiple reward models 16 Published as a conference paper at ICLR 2026 for re-annotation and training (Lee et al., 2025), Semi-DPO addresses the noise label problem caused by multi-dimensional preference conflicts in DPO datasets. We reclassify the dataset into clean labeled data and noisy unlabeled data. Following the p...

2026
[19]

They must share a latent space with the diffusion model they are training, which requires a shared V AE encoder

have a significant limitation: they are architecturally specific. They must share a latent space with the diffusion model they are training, which requires a shared V AE encoder. As different generative models (e.g., SD1.5 and SDXL) use different V AEs, a latent reward model trained for one is incompatible with another. This gives them great power but pre...

2023
[20]

for data fil- tering. This approach is motivated by the study that introduced the Multi-dimensional Preference Score (MPS) (Zhang et al., 2024), which constructed a dataset reflecting real human preferences by 17 Published as a conference paper at ICLR 2026 Table 6: The evaluation of MPS and scoring functions for the prediction of multi- dimensional human...

2024
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The x-axis of each subplot represents the annotated real human preferences, and the y-axis denotes the model’s predictions

74.24 83.86 83.87 85.18 R=-0.002 R=0.540 R=-0.155 R=0.573 R=0.439 R=0.345 R=0.105 R=0.702 R=0.766 R=0.710 R=0.864 R=0.185 PickScoreMPS (Ours) CLIP score Overall score Aesthetic Semantic alignment Detail quantity Figure 4:Correlation between real user preferences and model predictions. The x-axis of each subplot represents the annotated real human preferen...

2024
[22]

Meanwhile, models such as HPSv2 (Wu et al., 2023a), ImageReward (Xu et al., 2023), and PickScore (Kirstain et al.,

shows a strong correlation with the semantic alignment dimension, while Aesthetic Score (Schuhmann et al., 2022a) is highly correlated with the aesthetics dimension. Meanwhile, models such as HPSv2 (Wu et al., 2023a), ImageReward (Xu et al., 2023), and PickScore (Kirstain et al.,

2023
[23]

Cosmic hamburger in space

show higher consistency with the overall score. This finding indicates that no single model can comprehensively evaluate image quality. As shown in Table 6 and Figure 4(Both Table 6 and Figure 4 are reproduced from MPS paper. They are Tab. 4 and Fig. 5 in their original paper.), the MPS model shows the strongest cor- relation with multi-dimensional human ...

2026
[24]

This is significantly more efficient than the 19 Published as a conference paper at ICLR 2026 standard Diffusion-DPO baseline, which necessitates192 GPU hours

requires approximately132 GPU hours. This is significantly more efficient than the 19 Published as a conference paper at ICLR 2026 standard Diffusion-DPO baseline, which necessitates192 GPU hours. This efficiency gain stems directly from the dynamic thresholding mechanism described previously, which effectively filters out low-confidence samples from the ...

2026