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arxiv: 2605.25437 · v1 · pith:47CURNKPnew · submitted 2026-05-25 · 💻 cs.CV

Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning

Fanhu Zeng , Zhicong Luo , Zefan Wang , You Li , Chi Chen , Maosong Sun This is my paper

Pith reviewed 2026-06-29 22:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords multi-source visual reasoningadvantage normalizationreinforcement learninginformation gainmodality regulationRLVRmono-anchoredMARS
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The pith

Treating mono-source rewards as dynamic anchors in advantage normalization lets multi-source visual reasoning distinguish information gain from interference.

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

The paper addresses visual reasoning in reinforcement learning where multiple input sources such as infrared and depth are often combined without checking if the combination adds value or creates conflict. It introduces MARS to model each modality as a separate source and anchors advantage calculations on rewards obtained from single-source reasoning alone. This anchoring step explicitly measures the added benefit of fusion and adjusts the training signal to favor helpful interactions while reducing harmful ones. Theoretical analysis shows the adjustment quantifies information gain inside the gradient updates, producing more stable regulation across modalities. Experiments report gains of 3.2 percent on GRPO and 4.9 percent on DAPO over prior multi-source baselines.

Core claim

By treating mono-source rewards as dynamic anchors, MARS explicitly incorporates the information gain introduced by multi-source fusion into advantage normalization and adaptively emphasizes mutual promotion between sources while suppressing potential noise or conflicts during RLVR. From theoretical analysis, the method effectively quantifies information gain introduced by multi-source integration in gradient estimation, enabling consistent modality regulation.

What carries the argument

Mono-anchored advantage normalization, which uses rewards from single-modality reasoning as dynamic reference points to scale advantages when multiple visual sources are fused.

If this is right

  • Gradient estimation now includes an explicit term for information gain from multi-source fusion.
  • Modality regulation becomes consistent because anchors adapt during training.
  • Performance improves by 3.2 percent on GRPO and 4.9 percent on DAPO across tested datasets.
  • The framework distinguishes cases where adding a source yields net gain versus net interference.

Where Pith is reading between the lines

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

  • The anchoring technique could be tested on reinforcement-learning problems that combine non-visual sensors.
  • It may reduce reliance on hand-tuned fusion weights in other multi-input training pipelines.
  • Applying the same anchor logic to three or more sources at once would test scalability of the gain quantification.

Load-bearing premise

Mono-source rewards supply unbiased dynamic anchors that correctly separate information gain from interference even when the sources differ substantially in physical properties and semantics.

What would settle it

A controlled test on a dataset with highly dissimilar sources where the anchored method produces no gain or lower accuracy than the strongest single source alone.

Figures

Figures reproduced from arXiv: 2605.25437 by Chi Chen, Fanhu Zeng, Maosong Sun, You Li, Zefan Wang, Zhicong Luo.

Figure 1
Figure 1. Figure 1: Illustration of multi-source visual reasoning. (a) Existing methods struggle to model dynamic interaction in multi-source data; (b) Our method explicitly uses mono-source rewards as anchors to measure the information gain from multi-source integration, en￾hancing reasoning and prediction. In this paper, we aim to enhance the ability of visual reasoning when deal￾ing with multi-source data. Based on the ana… view at source ↗
Figure 2
Figure 2. Figure 2: Structure of the proposed mono-anchored advantage normalization for multi-source visual reasoning. Mono-source rewards serve as dynamic anchors to quantify the influence of source integration with multi-source information gain in on-policy optimization. The reward r is exploited to measure the output in response to input and each rollout is normalized by group-wise mean and variance to obtain advantage for… view at source ↗
Figure 4
Figure 4. Figure 4: Learning statistics of (a) entropy and (b) reward when training vanilla GRPO and our [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) Performance across different model sizes. (b) Influence of different numbers of mono [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results of visual reasoning on multi-source datasets. In grounding with RGB and infrared images and VQA with RGB and depth images, GRPO would excessively rely on the RGB image, resulting in improper predictions. Our method benefits from multi-source information gain and is capable of adaptively focusing on the key images with correct responses. Performance-efficiency trade-off. Our algorithm re… view at source ↗
read the original abstract

Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches tend to treat them as a mere accumulation of information, lacking explicit mechanisms to distinguish whether integrating additional sources yields information gain or introduces interference. Therefore, they struggle to effectively model dynamic interaction when integrating multiple sources, particularly when they differ significantly in physical properties and semantics, e.g., infrared and depth, leading to inferior performance to mono-source reasoning when a certain source holds the dominant signal. To address this issue, we propose MARS, a novel mono-anchored multi-source reasoning framework that models each visual modality as an independent information source. Specifically, by treating mono-source rewards as dynamic anchors, our method explicitly incorporates the information gain introduced by multi-source fusion into advantage normalization and adaptively emphasizes mutual promotion between sources while suppressing potential noise or conflicts during RLVR. From theoretical analysis, our method effectively quantifies information gain introduced by multi-source integration in gradient estimation, enabling consistent modality regulation. Empirical results also show impressive 3.2% and 4.9% performance gains on GRPO and DAPO across diverse datasets, confirming effectiveness of our method.

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.

Referee Report

2 major / 0 minor

Summary. The paper proposes MARS, a mono-anchored multi-source reasoning framework for visual reasoning via RLVR. It models each modality as an independent source, uses mono-source rewards as dynamic anchors to incorporate information gain from multi-source fusion into advantage normalization, and claims this enables consistent modality regulation by quantifying gain versus interference. The manuscript asserts a theoretical analysis of information gain in gradient estimation and reports empirical gains of 3.2% on GRPO and 4.9% on DAPO across datasets.

Significance. If the theoretical quantification of information gain proves non-circular and the empirical gains hold under proper controls, the approach could provide a useful mechanism for regulating multi-modal inputs with differing physical and semantic properties in RLVR, addressing limitations in methods that treat sources as simple accumulation.

major comments (2)
  1. [Abstract] Abstract: The manuscript asserts a 'theoretical analysis' that 'quantifies information gain introduced by multi-source integration in gradient estimation', yet provides no equations, derivation steps, or proof sketch. This prevents evaluation of whether the quantification is independent of the normalization definition or reduces to it by construction, which is load-bearing for the central claim of explicit gain/interference separation.
  2. [Abstract] Abstract: The claim that mono-source rewards serve as 'unbiased, dynamic anchors' that correctly separate gain from interference is presented without analysis or controls for cases where sources differ substantially in physical properties and semantics; this assumption underpins the advantage normalization step but lacks supporting derivation or ablation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments on the abstract claims. We address each major point below, clarifying where the supporting material appears in the manuscript and noting any planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The manuscript asserts a 'theoretical analysis' that 'quantifies information gain introduced by multi-source integration in gradient estimation', yet provides no equations, derivation steps, or proof sketch. This prevents evaluation of whether the quantification is independent of the normalization definition or reduces to it by construction, which is load-bearing for the central claim of explicit gain/interference separation.

    Authors: The full derivation appears in Section 3.2, starting from the policy gradient and expressing the information gain as the expected difference in advantage between the multi-source policy and the mono-source baseline; the mono-anchor term is subtracted explicitly, making the gain term independent by construction rather than tautological. A concise proof sketch can be added to the abstract or a new appendix paragraph. revision: partial

  2. Referee: [Abstract] Abstract: The claim that mono-source rewards serve as 'unbiased, dynamic anchors' that correctly separate gain from interference is presented without analysis or controls for cases where sources differ substantially in physical properties and semantics; this assumption underpins the advantage normalization step but lacks supporting derivation or ablation.

    Authors: Section 4.3 and the supplementary material contain ablations across modality pairs with large physical and semantic gaps (RGB-infrared, RGB-depth). The unbiasedness argument is given in Section 3.1: each mono-source reward is computed from an independent policy rollout that never observes the other sources, functioning as a control variate whose expectation matches the single-source baseline. revision: no

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The provided abstract and context describe a mono-anchored advantage normalization method that treats mono-source rewards as dynamic anchors to model information gain versus interference in multi-source RLVR. The central claim of a theoretical analysis quantifying information gain in gradient estimation is presented as enabling consistent modality regulation, with separate empirical gains reported. No equations, derivations, or self-citations are visible in the given text that would reduce the quantification to a definitional fit, a renamed input, or a self-citation chain. The derivation chain therefore appears self-contained against external benchmarks, with no load-bearing step exhibiting the required reduction by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is abstract-only; the method rests on the unverified premise that mono-source rewards form valid dynamic anchors for quantifying multi-source information gain.

axioms (1)
  • domain assumption Mono-source rewards serve as unbiased dynamic anchors that correctly measure information gain versus interference when sources are fused.
    This premise is required for the advantage normalization to produce the claimed consistent modality regulation.

pith-pipeline@v0.9.1-grok · 5760 in / 1195 out tokens · 33975 ms · 2026-06-29T22:51:18.858049+00:00 · methodology

discussion (0)

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