arxiv: 2605.13467 · v1 · submitted 2026-05-13 · 💻 cs.CL

Recognition: no theorem link

PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning

Chang D. Yoo, Chong Luo, Eunseop Yoon, Gwanhyeong Koo, Hee Suk Yoon, Ji Woo Hong, Mark Hasegawa-Johnson, Qi Dai, SooHwan Eom

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:16 UTC · model grok-4.3

classification 💻 cs.CL

keywords vision-language reasoningreinforcement learningconfidence rewardreward decompositionunsupervised clusteringperception stepsreasoning steps

0 comments

The pith

Decomposing confidence rewards into perception and reasoning clusters improves vision-language model training.

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

The paper argues that a single global confidence reward works poorly for vision-language reasoning because it mixes sparse visual perception steps with dense textual reasoning steps, distorting the training signal for the visual part. PDCR addresses this by first computing a model-internal Visual Dependence Score for each step, then using unsupervised clustering to separate the steps into two skill groups. It then normalizes the confidence gains separately inside each group to produce a correctly scaled advantage for both perception and reasoning. Experiments show this decomposed reward beats both the naive global version and standard sparse outcome rewards on standard V-L benchmarks. The method requires no external models or labeled step annotations.

Core claim

PDCR solves mixture-induced signal degradation in RLVR for vision-language tasks by introducing a Visual Dependence Score to quantify each step's visual reliance, applying unsupervised clustering to partition steps into perception and reasoning clusters, and computing decomposed advantages through intra-cluster normalization of confidence gains, which supplies a stable and properly scaled training signal aligned with the heterogeneous structure of the task.

What carries the argument

Visual Dependence Score plus unsupervised clustering that enables intra-cluster normalization of confidence gains for the decomposed reward.

If this is right

PDCR produces higher benchmark scores than both global-reward and sparse-reward baselines on vision-language reasoning tasks.
Intra-cluster normalization supplies correctly scaled signals for perception steps that would otherwise be drowned out by textual steps.
The approach delivers step-level guidance while remaining fully model-intrinsic and free of external verifiers.
The decomposition aligns the reward structure directly with the mix of sparse visual and dense textual components in the task.

Where Pith is reading between the lines

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

The same clustering idea could be tested on other mixed-density multimodal tasks such as video-text or audio-text reasoning.
Preventing one skill type from dominating the reward signal may allow stable training on longer multi-step chains.
Because the decomposition is unsupervised, it opens the possibility of models that discover their own skill partitions during training.

Load-bearing premise

The unsupervised clustering driven by the model-internal Visual Dependence Score accurately partitions steps into perception and reasoning without any labeled supervision or external verification.

What would settle it

Training a model with PDCR on a V-L benchmark and measuring no gain in final accuracy or step-level metrics compared with the global-reward baseline.

Figures

Figures reproduced from arXiv: 2605.13467 by Chang D. Yoo, Chong Luo, Eunseop Yoon, Gwanhyeong Koo, Hee Suk Yoon, Ji Woo Hong, Mark Hasegawa-Johnson, Qi Dai, SooHwan Eom.

**Figure 2.** Figure 2: The baseline dense reward pipeline. For N rollouts, a sparse Outcome Reward (R (i) ) is computed from the final answer’s correctness. Concurrently, the model’s stepwise groundtruth confidence (c (i) k ) is used to derive a dense Process-level Reward (g (i) k ) (i.e., the confidence gain). These two rewards are then converted into an Outcome-based Advantage (A (i) O ) and a globallynormalized Process-le… view at source ↗

**Figure 3.** Figure 3: An illustration of our core observations and the mixture-induced signal degradation problem. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Overview of our PDCR (Perception-Decomposed Confidence Reward) framework. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Our dynamic thresholding (Otsu’s method) is more accu [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: Training dynamics, cost, and efficiency comparison. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Visual Perturbation Strategies Evaluated for Skill Decomposition. To calculate the Visual Dependence Score ( [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗

read the original abstract

Reinforcement Learning with Verifiable Rewards (RLVR) traditionally relies on a sparse, outcome-based signal. Recent work shows that providing a fine-grained, model-intrinsic signal (rewarding the confidence growth in the ground-truth answer) effectively improves language reasoning training by providing step-level guidance without costly external models. While effective for unimodal text, we find that naively applying this global reward to vision-language (V-L) reasoning is a suboptimal strategy, as the task is a heterogeneous mix of sparse visual perception and dense textual reasoning. This global normalization creates mixture-induced signal degradation, where the training signal for visual steps is statistically distorted by the predominant textual steps. We propose Perception-Decomposed Confidence Reward (PDCR), a framework that solves this by aligning the reward structure with the task's heterogeneous nature. PDCR first performs an unsupervised skill decomposition, introducing a model-internal Visual Dependence Score to quantify visual reliance and applying a clustering algorithm to separate perception and reasoning steps. Based on this, PDCR computes a decomposed advantage by normalizing confidence gains within each skill cluster. This intra-cluster normalization provides a stable, correctly-scaled signal for both perception and reasoning. We demonstrate that PDCR outperforms the naive, global-reward formulation and sparse-reward baselines on key V-L reasoning benchmarks.

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

read the letter

The paper flags a real issue with global confidence rewards on mixed V-L tasks and proposes unsupervised decomposition via Visual Dependence Score clustering, but without validation on the clusters the performance claims are hard to attribute to the intended mechanism. They observe that vision-language reasoning mixes sparse visual perception steps with dense textual reasoning, so a single global normalization on confidence growth distorts the signal for the visual parts. PDCR computes a model-internal Visual Dependence Score, clusters steps unsupervised into perception and reasoning groups, and then normalizes the confidence advantage inside each cluster. This intra-cluster approach is presented as a direct fix for the mixture-induced degradation. The motivation is clear and the method stays within model-internal signals, avoiding extra verifiers or labels. If the clusters separate the skills reliably, the normalized advantages should give more balanced step-level guidance than a global baseline. The abstract states that PDCR beats both the naive global formulation and sparse-reward baselines on key V-L benchmarks. The main limitation is the missing check on the clustering itself. No human inspection, proxy-task correlation, or ablation on cluster stability is described, so it remains possible that any gains come from altered normalization variance rather than from correctly aligned perception-reasoning signals. The abstract supplies no quantitative results, error bars, or ablation tables, leaving the size and reliability of the improvement unevaluated. This work is aimed at researchers building RL pipelines for multimodal reasoning who need step-level signals without external models. A reader interested in reward design for heterogeneous tasks would find the problem statement and the proposed normalization useful to test. It deserves peer review because the core limitation is well-motivated and the method is straightforward to reproduce, even if the clustering validation and experimental details will require referee scrutiny.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Perception-Decomposed Confidence Reward (PDCR) for RLVR in vision-language reasoning. It argues that global confidence rewards suffer from mixture-induced signal degradation because V-L tasks mix sparse visual perception steps with dense textual reasoning steps. PDCR introduces a model-internal Visual Dependence Score, applies unsupervised clustering to separate perception and reasoning steps, and computes intra-cluster normalized advantages on confidence gains. The authors claim this outperforms both naive global-reward formulations and sparse-reward baselines on key V-L reasoning benchmarks.

Significance. If the unsupervised decomposition is reliable, PDCR supplies a parameter-free, model-intrinsic mechanism for skill-aligned step-level rewards in multimodal settings. This could reduce reliance on external verifiers while mitigating variance distortion across heterogeneous step types, offering a practical advance for training vision-language models on reasoning tasks.

major comments (2)

[Method] Method section (description of Visual Dependence Score and clustering): the central claim that intra-cluster normalization supplies a 'correctly-scaled signal' rests on the assumption that the unsupervised partitions accurately separate perception from reasoning. No validation, human labels, proxy-task correlation, or ablation on cluster stability/quality is reported, so it remains possible that reported gains arise from altered normalization variance rather than the intended decomposition.
[Experiments] Experiments section: the abstract asserts outperformance on benchmarks, yet the manuscript supplies no quantitative results, error bars, baseline implementations, statistical significance tests, or ablation studies isolating the contribution of the decomposition. Without these, the headline claim cannot be evaluated.

minor comments (2)

[Method] Notation for the Visual Dependence Score is introduced without an explicit equation or algorithmic pseudocode, making the clustering procedure difficult to reproduce.
[Abstract] The abstract states 'key V-L reasoning benchmarks' without naming them or reporting any numbers; this should be expanded even in the abstract for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on our manuscript. We address the two major comments below and will revise the paper accordingly to strengthen the validation of the decomposition and the presentation of experimental evidence.

read point-by-point responses

Referee: [Method] Method section (description of Visual Dependence Score and clustering): the central claim that intra-cluster normalization supplies a 'correctly-scaled signal' rests on the assumption that the unsupervised partitions accurately separate perception from reasoning. No validation, human labels, proxy-task correlation, or ablation on cluster stability/quality is reported, so it remains possible that reported gains arise from altered normalization variance rather than the intended decomposition.

Authors: We agree that demonstrating the quality of the unsupervised partitions is essential to support the claim of skill-aligned rewards. The Visual Dependence Score is computed from model-internal attention patterns over visual tokens, and clustering is performed via k-means on these scores. In the revision we will add: (1) stability analysis across random seeds and different numbers of clusters, (2) correlation of cluster assignments with proxy signals such as per-step visual grounding accuracy on a held-out VQA subset, and (3) a small human annotation study on 200 randomly sampled steps to measure agreement between clusters and human perception/reasoning labels. These additions will directly test whether the decomposition isolates the intended step types rather than merely changing normalization variance. revision: yes
Referee: [Experiments] Experiments section: the abstract asserts outperformance on benchmarks, yet the manuscript supplies no quantitative results, error bars, baseline implementations, statistical significance tests, or ablation studies isolating the contribution of the decomposition. Without these, the headline claim cannot be evaluated.

Authors: We apologize for the insufficient detail in the experimental reporting. The full manuscript does contain benchmark results comparing PDCR against global-reward and sparse-reward baselines on standard V-L reasoning datasets, but these were not presented with sufficient rigor. In the revised version we will: (1) report mean performance with standard deviation across 5 random seeds, (2) include statistical significance via paired t-tests against each baseline, (3) provide explicit implementation details and hyperparameters for all baselines, and (4) add an ablation table that isolates the effect of intra-cluster normalization versus global normalization while keeping the decomposition fixed. These changes will make the quantitative claims fully evaluable. revision: yes

Circularity Check

0 steps flagged

No significant circularity in PDCR derivation chain

full rationale

The paper derives PDCR from model-internal Visual Dependence Score, unsupervised clustering of steps into perception vs. reasoning, and intra-cluster normalization of confidence gains. These steps are defined directly from the model's own signals without reducing to fitted parameters drawn from the target benchmarks, self-citations that bear the central claim, or any renaming of known results. Performance is measured on external V-L benchmarks, keeping the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that vision-language reasoning is a heterogeneous mixture of perception and reasoning steps whose signals are distorted by global normalization; it introduces the Visual Dependence Score and cluster-based decomposition as new constructs without external validation.

axioms (1)

domain assumption Vision-language reasoning is a heterogeneous mix of sparse visual perception and dense textual reasoning steps whose signals are statistically distorted by global normalization.
Explicitly stated as the reason the naive global-reward approach is suboptimal.

invented entities (1)

Visual Dependence Score no independent evidence
purpose: Quantify each step's reliance on visual input to enable unsupervised clustering
New model-internal metric introduced to perform the skill decomposition.

pith-pipeline@v0.9.0 · 5554 in / 1294 out tokens · 40725 ms · 2026-05-14T19:16:10.963422+00:00 · methodology

discussion (0)

Reference graph

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[57]

Training Procedure Pseudocode 2
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Experimental Results on Additional Model Backbone 3
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Segmentation Detail 3
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Annotation Setup 4 15.2

Label Acquisition for Skill Analysis 4 15.1 . Annotation Setup 4 15.2 . Validation of Label Quality 4 15.3 . Qualitative Examples of Skill Decomposition 5
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Training Framework and Hyperparameters 6 16.2

Implementation Details 6 16.1 . Training Framework and Hyperparameters 6 16.2 . Prompt Template for Training and Inference 7
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Ablation Study on Visual Dependence Calculation for Skill Decomposition 8
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Qualitative Comparisons of Generated Reasoning 9
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Limitations and Future Works 14
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Broader Impact This work introduces a framework for improving the reasoning capabilities of multimodal Large Language Models. By lever- aging the model’s intrinsic confidence dynamics, our method provides fine-grained, step-level supervision, and decomposes this signal to align with the heterogeneous skills of perception and reasoning. This is achieved wi...
[66]

Furthermore, our experiments utilize only publicly available datasets and do not involve the collection of sensitive or personally identifiable information

Ethics Statement This research strictly adheres to academic integrity standards, ensuring all prior work is properly cited and acknowledged. Furthermore, our experiments utilize only publicly available datasets and do not involve the collection of sensitive or personally identifiable information
[67]

This pseudocode provides a step-by-step specification of the method summarized in Section 5

Training Procedure Pseudocode We outline our Perception-Decomposed Confidence Reward (PDCR) training procedure in Algorithm 1. This pseudocode provides a step-by-step specification of the method summarized in Section 5. The highlighted lines indicate the additional processing steps introduced in our proposed PDCR compared to PACR [42]. Algorithm 1:Percept...
[68]

As shown in Table 3, PDCR demonstrates generalization to this stronger backbone, achieving a final average score of 59.1

Experimental Results on Additional Model Backbone We further evaluate PDCR on the recently released Qwen3-VL-8B-Instruct (implementation details are outlined in Appendix 16). As shown in Table 3, PDCR demonstrates generalization to this stronger backbone, achieving a final average score of 59.1. This performance outperforms the sparse GRPO baseline (58.3,...
[69]

Step 1:”, “Step 2:

Segmentation Detail A prerequisite for a process-based reward framework is the segmentation of the reasoning trajectory τ (i) into a discrete sequence of steps{h (i) k }Ki k=1. The step is the fundamental unit to which a reward or advantage is assigned. Previous work in process-reward modeling has adopted several strategies to define this unit: • Supervis...
[70]

visual perception

Label Acquisition for Skill Analysis To empirically validate the heterogeneous nature of V-L reasoning ([Observation 1] in Section 4) and the effectiveness of our unsupervised skill decomposition (Section 5.1), we required a set of ground truth skill labels. Since no existing dataset provides step-level distinctions between perception and reasoning, we co...

2025
[71]

They achieved a Cohen’s Kappa of κ= 0.82 , indicating that the binary distinction between perception and reasoning is well-defined and unambiguous to humans

Human Inter-Annotator Agreement:Two human experts independently annotated a random subset of 100 steps. They achieved a Cohen’s Kappa of κ= 0.82 , indicating that the binary distinction between perception and reasoning is well-defined and unambiguous to humans
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Midnight

Model-Human Alignment:We compared the primary gpt-5 annotations against the human consensus on the same subset. The model achieved a Kappa score of κ= 0.79 (Table 4). This high alignment confirms that the model effectively acts as a reliable proxy for human judgment, correctly adhering to the strict definitions provided in the prompt. Table 4.Inter-Annota...
[73]

Training Framework and Hyperparameters We perform all experiments using theEasyR1framework

Implementation Details 16.1. Training Framework and Hyperparameters We perform all experiments using theEasyR1framework. Consistent with the R1-Zero style training [ 9], we apply Reinforce- ment Learning with Verifiable Rewards (RLVR) directly on the base model, bypassing any Supervised Fine-Tuning (SFT) stage. This ensures that the reasoning behaviors we...

2048
[74]

Visual Perturbation Strategies Evaluated for Skill Decomposition

Ablation Study on Visual Dependence Calculation for Skill Decomposition Figure 7. Visual Perturbation Strategies Evaluated for Skill Decomposition. To calculate the Visual Dependence Score (V (i) k , Eq. 7) , we compare the model’s probability on the(a) Originalimage against four baselines:(b) White(Strategy adopted in main text),(c) Gaussian Blur,(d) Gau...
[75]

All examples presented were generated by the final policies trained on theQwen2.5-VL-7B-Instruct backbone

Qualitative Comparisons of Generated Reasoning In this section, we present side-by-side comparisons between our proposed PDCR and baselines (GRPO [28], PACR [42]) on V- L reasoning tasks. All examples presented were generated by the final policies trained on theQwen2.5-VL-7B-Instruct backbone. These examples highlight the core benefit of our decomposed re...
[76]

It looks like a pyramid-like structure with a base and layers decreasing upwards

Visual Inspection: The figure appears to be a 3D structure with cubes stacked in a somewhat organized manner. It looks like a pyramid-like structure with a base and layers decreasing upwards
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- The next layer up has 5 cubes

Counting Layers: - The base layer looks like it has 6 cubes. - The next layer up has 5 cubes. - The layer above that has 4 cubes. - The top layer has 3 cubes
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Summing Up: Adding these together gives us 6 + 5 + 4 + 3 = 18 cubes
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(b) 24 is significantly more

Comparison with Options: (a) 17 is close but slightly less. (b) 24 is significantly more. (c) 7 is much less. (d) 39 is much more. Given the visual inspection and the summing up, the closest reasonable estimate is 17.</think> a Generated Sample 2 by PDCR (ours) Question: Which of the following options is a reasonable estimate of the number of cubes in the...
[80]

Computational Cost.The primary limitation of our framework is the computational overhead during the training phase

Limitations and Future Works Our work presents a promising direction for self-rewarding V-L models, but has limitations that offer avenues for future research. Computational Cost.The primary limitation of our framework is the computational overhead during the training phase. As shown in Figure 6-(b) of main paper, both PDCR and the dense-reward baseline (...