arxiv: 2604.09349 · v1 · submitted 2026-04-10 · 💻 cs.CV · cs.AI· cs.CL

Recognition: 2 theorem links

· Lean Theorem

Visually-Guided Policy Optimization for Multimodal Reasoning

Feng Xiong, Liang Lin, Man Zhang, Xiangxiang Chu, Xuecai Hu, Yanlin Wang, Yong Wang, Zengbin Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:20 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CL

keywords visual attention compensationpolicy optimizationmultimodal reasoningvision-language modelsvisual faithfulnessadvantage re-weightingreinforcement learningtemporal visual forgetting

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The pith

Visually-guided policy optimization amplifies relevant visual tokens and reweights advantages to counter forgetting in vision-language models.

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

The paper tries to establish that standard reinforcement learning on vision-language models lets visual information drop away during long reasoning chains because text signals dominate the updates. VGPO fixes this by first using visual similarity scores to find and strengthen the most relevant image tokens at each step, then raising the expected visual contribution in later stages to fight progressive loss of focus. It further adjusts the training signal so that reasoning paths and individual tokens with stronger visual engagement get higher weight in the gradient updates. If correct, this produces models that stay grounded in the image content even when solving multi-step problems like diagram-based math. A sympathetic reader would care because many current VLMs produce fluent but visually unfaithful answers on tasks where the image actually matters.

Core claim

VGPO introduces a Visual Attention Compensation mechanism that localizes and amplifies visual cues through similarity metrics while progressively elevating visual expectations across reasoning steps to counteract temporal forgetting, and pairs it with a dual-grained advantage re-weighting strategy that boosts tokens with high visual activation within each trajectory and entire trajectories that accumulate more visual focus overall, yielding measurably stronger visual activation and higher accuracy on mathematical multimodal reasoning and visual-dependent tasks.

What carries the argument

The Visual Attention Compensation mechanism, which uses visual similarity to localize and amplify cues while raising visual expectations to prevent forgetting, together with dual-grained advantage re-weighting at the intra-trajectory token level and inter-trajectory level.

If this is right

Superior accuracy on mathematical multimodal reasoning benchmarks compared with standard RLVR.
Improved results on tasks that require ongoing visual grounding rather than text-only shortcuts.
Higher measured activation on relevant visual tokens throughout the full reasoning chain.
Reduced drop-off in visual focus as the number of reasoning steps grows.
The same compensation and re-weighting logic can be applied on top of existing verifiable-reward training pipelines.

Where Pith is reading between the lines

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

The approach may extend to non-reasoning VLM tasks such as detailed visual question answering where sustained image focus is needed.
Explicit visual guidance during optimization could decrease the frequency of answers that contradict visible image content.
Varying the strength of the similarity-based amplification across different image types could identify when the compensation is most or least effective.

Load-bearing premise

That amplifying visual cues through similarity metrics and re-weighting advantages according to visual activation levels will reliably reduce forgetting without destabilizing the policy or harming text-based reasoning performance.

What would settle it

Training with VGPO and then measuring that attention maps on visual tokens show no sustained increase or that accuracy on visual-dependent reasoning benchmarks fails to exceed the standard RLVR baseline.

Figures

Figures reproduced from arXiv: 2604.09349 by Feng Xiong, Liang Lin, Man Zhang, Xiangxiang Chu, Xuecai Hu, Yanlin Wang, Yong Wang, Zengbin Wang.

**Figure 2.** Figure 2: Analysis of the inference nature of multimodal reasoning trajectory (based on Qwen2.5-VL-7B ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of Visually-Guided Policy Optimization framework. Given query and image, (a) VGPO firstly utilizes the intrinsic hidden state similarity between generated tokens and visual prototype to derive a Visual Focus Score for visual token localization. (b) Then, Visual Attention Compensation (VAC) mechanism leverages this score to re-focus visual tokens, while progressively elevating visual expectations a… view at source ↗

**Figure 4.** Figure 4: Training dynamics based on Qwen2.5-VL7B: (a) training rewards and (b) validation accuracy on MMK12 (Meng et al., 2025) across GRPO (Shao et al., 2024), DAPO (Yu et al., 2025), and our VGPO. LogicVista (Xiao et al., 2024), SuperClevr Counting (Li et al., 2023), MMMU-Pro (Yue et al., 2025), and MathVerse-V (Zhang et al., 2024b). Specifically, we adopt these datasets from PAPOEval (Wang et al., 2025e), whi… view at source ↗

**Figure 5.** Figure 5: Ablation study of training dynamics of the [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Comparison of the vision attention ratio dis [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 8.** Figure 8: Training dynamics of Qwen2.5-VL-32B: (a) training rewards and (b) validation accuracy on MMK12 (Meng et al., 2025) across DAPO (Yu et al., 2025), and our VGPO. suggests that our visual-guided strategy effectively reduces variance and stabilizes the optimization process for large-scale models. Consistent with the rewards, [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

read the original abstract

Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the inherent text-dominated nature of VLMs often leads to insufficient visual faithfulness, characterized by sparse attention activation to visual tokens. More importantly, our empirical analysis reveals that temporal visual forgetting along reasoning steps exacerbates this deficiency. To bridge this gap, we propose Visually-Guided Policy Optimization (VGPO), a novel framework to reinforce visual focus during policy optimization. Specifically, VGPO initially introduces a Visual Attention Compensation mechanism that leverages visual similarity to localize and amplify visual cues, while progressively elevating visual expectations in later steps to counteract visual forgetting. Building on this mechanism, we implement a dual-grained advantage re-weighting strategy: the intra-trajectory level highlights tokens exhibiting relatively high visual activation, while the inter-trajectory level prioritizes trajectories demonstrating superior visual accumulation. Extensive experiments demonstrate that VGPO achieves better visual activation and superior performance in mathematical multimodal reasoning and visual-dependent tasks.

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

VGPO adds a visual similarity compensation step and dual-grained re-weighting to RLVR for VLMs to fight attention forgetting, but the abstract supplies no numbers or controls so the gains remain unproven.

read the letter

The main thing to know is that VGPO introduces a Visual Attention Compensation mechanism that uses visual similarity to amplify relevant tokens and progressively raises visual expectations later in the chain, then applies intra-trajectory and inter-trajectory advantage re-weighting based on visual activation levels. This targets the text-heavy bias and temporal forgetting the authors observed in standard RLVR for vision-language models. The framing is new in the cited RLVR literature and gives a concrete way to inject visual guidance into policy updates for tasks like multimodal math reasoning. The paper earns credit for naming the forgetting problem explicitly and for building a mechanism that tries to counteract it without just adding more text supervision. The approach is specific enough that it could be tested and extended by others working on the same issue. The soft spots are straightforward: the abstract reports no quantitative results, no baselines, no error bars, and no ablations, so there is no way to judge whether the similarity metric actually surfaces semantically useful visual content or just low-level patterns. The re-weighting could also favor visually salient but logically weak trajectories, and nothing is said about whether text-only reasoning performance drops as a side effect. The stress-test concern lands because the causal link between these visual adjustments and reliable reasoning gains is asserted rather than shown with the kind of controls that would make the claim convincing. This is for people already working on RL fine-tuning of VLMs who want ideas for visual faithfulness fixes. A reader in that niche could extract the technique and try it, but the current write-up is too light on evidence to stand on its own. I would send it for peer review because the underlying problem is real and the proposed components are distinct enough that referees can check the experiments and assumptions directly.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes Visually-Guided Policy Optimization (VGPO) to address insufficient visual faithfulness and temporal visual forgetting in vision-language models under reinforcement learning with verifiable rewards. VGPO introduces a Visual Attention Compensation mechanism that uses visual similarity to localize and amplify cues while progressively elevating visual expectations across reasoning steps, combined with a dual-grained advantage re-weighting strategy (intra-trajectory highlighting of high visual activation tokens and inter-trajectory prioritization of trajectories with superior visual accumulation). The paper claims this yields improved visual activation and superior performance on mathematical multimodal reasoning and visual-dependent tasks.

Significance. If the empirical results hold under rigorous validation, VGPO could offer a targeted method for improving visual grounding in VLM reasoning pipelines without explicit trade-offs against text-based performance. The progressive elevation of visual expectations and dual-grained re-weighting constitute concrete, potentially reusable techniques for mitigating modality imbalance in sequential policy optimization.

major comments (3)

Abstract: the assertion that 'extensive experiments demonstrate that VGPO achieves better visual activation and superior performance' supplies no quantitative metrics, baselines, datasets, ablations, or error bars, so the data-to-claim link cannot be evaluated and the central performance claim remains unsupported.
Abstract: the Visual Attention Compensation mechanism is described only at the level of 'leverages visual similarity to localize and amplify visual cues' and 'progressively elevating visual expectations in later steps,' with no specification of the similarity metric (layer, distance, normalization), elevation schedule, or clipping, which are load-bearing for confirming that the signal captures task-relevant semantic content rather than low-level statistics.
Abstract: the dual-grained advantage re-weighting is introduced without implementation details or analysis of side effects (e.g., whether intra- or inter-trajectory factors can amplify logically flawed but visually salient trajectories or degrade text-only reasoning), directly undermining assessment of the assumption that the re-weighting reliably counters forgetting without destabilizing the policy.

minor comments (1)

Abstract: the motivation paragraph would benefit from a single concrete example or statistic illustrating the observed temporal visual forgetting to make the problem statement more tangible.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that the abstract would benefit from greater specificity to strengthen the link between claims and evidence, and we will revise it accordingly while preserving its concise nature. Below we respond point by point to the major comments.

read point-by-point responses

Referee: Abstract: the assertion that 'extensive experiments demonstrate that VGPO achieves better visual activation and superior performance' supplies no quantitative metrics, baselines, datasets, ablations, or error bars, so the data-to-claim link cannot be evaluated and the central performance claim remains unsupported.

Authors: We acknowledge that the current abstract does not include specific quantitative results. The full manuscript presents these details in the Experiments section, including performance on mathematical multimodal reasoning and visual-dependent tasks, comparisons against baselines, ablation studies, and variability measures. We will revise the abstract to incorporate a concise summary of key empirical outcomes and the evaluation setup to better support the central claim. revision: yes
Referee: Abstract: the Visual Attention Compensation mechanism is described only at the level of 'leverages visual similarity to localize and amplify visual cues' and 'progressively elevating visual expectations in later steps,' with no specification of the similarity metric (layer, distance, normalization), elevation schedule, or clipping, which are load-bearing for confirming that the signal captures task-relevant semantic content rather than low-level statistics.

Authors: The abstract summarizes the mechanism at a conceptual level. The full manuscript specifies the similarity computation, layer selection, normalization, elevation schedule, and any clipping in the method description. We will revise the abstract to include a brief, precise characterization of the similarity metric and elevation process. revision: yes
Referee: Abstract: the dual-grained advantage re-weighting is introduced without implementation details or analysis of side effects (e.g., whether intra- or inter-trajectory factors can amplify logically flawed but visually salient trajectories or degrade text-only reasoning), directly undermining assessment of the assumption that the re-weighting reliably counters forgetting without destabilizing the policy.

Authors: The abstract introduces the strategy at a high level. The manuscript provides the implementation formulas, hyperparameters, and ablation analysis examining effects on visual focus versus text reasoning stability. We will revise the abstract to note the dual-grained structure and its observed balance in policy optimization. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework presented as novel construction without reductions to inputs or self-citations.

full rationale

The provided abstract and context contain no equations, derivations, or self-citations. VGPO is introduced as a new framework with Visual Attention Compensation and dual-grained re-weighting to address empirically observed temporal visual forgetting. No load-bearing step reduces a prediction or result to a fitted parameter or prior self-citation by construction. The central claims rest on the proposed mechanisms and experimental outcomes rather than definitional equivalence or renamed inputs. This is the expected non-finding for a methods paper describing a new RLVR variant.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on domain assumptions about measurable visual attention dynamics and the effectiveness of similarity-based compensation; several implementation parameters are implied but unspecified in the abstract.

free parameters (2)

visual similarity amplification factor
The compensation mechanism relies on visual similarity to localize and amplify cues; the strength of this amplification is a tunable parameter.
visual expectation elevation schedule
Progressively elevating visual expectations across reasoning steps requires a schedule or rate parameter that is not detailed.

axioms (1)

domain assumption Visual attention in VLMs can be reliably quantified and compensated using similarity metrics between visual tokens.
The method presupposes that similarity-based localization accurately identifies important visual cues worth amplifying.

pith-pipeline@v0.9.0 · 5488 in / 1377 out tokens · 61566 ms · 2026-05-10T17:20:04.694682+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
we introduce an intrinsic metric based on hidden state similarity... S(h_i,t, mu_v) = (h_i,t)^T mu_v / (||h_i,t|| ||mu_v||) ... rho_i,t = 1/2 (S + 1)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Structured Role-Aware Policy Optimization for Multimodal Reasoning
cs.AI 2026-05 unverdicted novelty 7.0

SRPO refines GRPO into role-aware token-level advantages by emphasizing perception tokens based on visual dependency (original vs. corrupted inputs) and reasoning tokens based on consistency with perception, unified v...

Reference graph

Works this paper leans on

8 extracted references · 5 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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Hallucination of Multimodal Large Language Models: A Survey

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Visual Anchors

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By visually inspecting the image, it appears there are at least 10 vertical bars

Count the vertical bars: The image shows a series of vertical bars that form a fence. By visually inspecting the image, it appears there are at least 10 vertical bars. (visual counting error)
[7]

There are 10 vertical bars

Count the elements: There is one cow visible in the image. There are 10 vertical bars
[8]

Therefore, the total number of elements is: \boxed{11}

Calculate the total number of elements: Total elements = 10 + 1 = 11. Therefore, the total number of elements is: \boxed{11}. Failure Analysis: The model’s prediction fails due to an Object Counting Error. It correctly identifies the task of counting bars and adding the cow, but falters on the initial visual perception step, incorrectly counting 10 vertic...