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arxiv: 2512.08980 · v3 · submitted 2025-12-05 · 💻 cs.CV · cs.AI

Recognition: no theorem link

Training Multi-Image Vision Agents via End2End Reinforcement Learning

Chengqi Dong , Chuhuai Yue , Hang He , Rongge Mao , Fenghe Tang , S Kevin Zhou , Zekun Xu , Xiaohan Wang
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Jiajun Chai Guojun Yin
Authors on Pith no claims yet

Pith reviewed 2026-05-17 00:54 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords vision-language modelsreinforcement learningtool usemulti-image reasoningvisual agentstrajectory maskingend-to-end trainingvisual reflection tools
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The pith

IMAgent learns effective tool use for multi-image reasoning through pure end-to-end reinforcement learning without supervised fine-tuning data.

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

The paper introduces IMAgent, an open-source visual agent for fine-grained reasoning over single or multiple images. It counters the tendency of vision-language models to ignore visual content during long reasoning by adding two tools for reflection and verification. A two-layer motion trajectory masking strategy plus tool-use reward gain lets the base model acquire a useful tool-use policy solely through reinforcement learning. The approach also includes building a new visually rich multi-image QA dataset to fill training gaps. This yields state-of-the-art results on standard benchmarks while showing how tool use improves attention to images.

Core claim

Equipped with two dedicated tools for visual reflection and verification, IMAgent trains a base VLM end-to-end via reinforcement learning. A two-layer motion trajectory masking strategy and tool-use reward gain produce an effective tool-use paradigm without any supervised fine-tuning data. The method reveals that tool usage enhances performance by maintaining attention on image content and reaches SOTA results on single- and multi-image benchmarks.

What carries the argument

Two-layer motion trajectory masking strategy together with tool-use reward gain, which together shape the reinforcement learning signal to develop and sustain tool-use behavior.

Load-bearing premise

The combination of visual reflection and verification tools with the specific masking and reward design will cause the base VLM to learn and maintain an effective tool-use policy through reinforcement learning alone.

What would settle it

An ablation experiment in which removing the two-layer masking or the tool-use reward gain causes the model to stop using the reflection tools and produces no improvement over the base VLM on multi-image tasks.

Figures

Figures reproduced from arXiv: 2512.08980 by Chengqi Dong, Chuhuai Yue, Fenghe Tang, Guojun Yin, Hang He, Jiajun Chai, Rongge Mao, S Kevin Zhou, Xiaohan Wang, Zekun Xu.

Figure 1
Figure 1. Figure 1: We compared the attention proportions of newly generated tokens by Qwen2.5-VL-7B (a, b) and IMAgent (c, d) to the input [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Figure 2: Overview of IMAgent. Our model will automatically choose whether and how to use tools based on the actual problem. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A three-stage data construction pipeline based on multi-agent systems. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Action-Level and Trajectory-Level Mask. 3.3.2. Two-Level Mask Strategy To adapt to the training of visual tools, we design a two￾level mask strategy to enhance training stability (as illus￾trated in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Attention distribution across model layers. The first row [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Attention maps of the model using the visual confirma [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Demonstration of some typical tool use strategies. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of tools with and without trajectory masks. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

Recent VLM-based agents aim to replicate OpenAI O3's "thinking with images" via tool use, yet most open-source methods restrict inputs to a single image, limiting their applicability to real-world multi-image QA tasks. To address this gap, we propose IMAgent, an open-source visual agent trained with end-to-end reinforcement learning for fine-grained single/multi-image reasoning. During inference, VLMs tend to gradually neglect visual inputs; to mitigate this issue, we design two dedicated tools for visual reflection and verification, enabling the model to actively refocus attention on image content. Beyond that, we, for the first time, reveal how tool usage enhances agent performance from an attention perspective. Equipped with a carefully designed two-layer motion trajectory masking strategy and tool-use reward gain, IMAgent acquires an effective tool-use paradigm through pure reinforcement learning, eliminating the need for costly supervised fine-tuning data. To further unleash the inherent tool-usage potential of the base VLM and fill data gaps, we construct a challenging, visually enriched multi-image QA dataset via multi-agent system. Extensive experiments validate that IMAgent achieves SOTA performance across mainstream single and multi-image benchmarks, and our in-depth analysis offers actionable insights for the community. Code and data will be released soon.

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

3 major / 2 minor

Summary. The paper introduces IMAgent, a VLM-based agent for fine-grained single- and multi-image reasoning trained entirely via end-to-end reinforcement learning. It adds two dedicated tools (visual reflection and verification) to counteract gradual neglect of visual tokens, introduces a two-layer motion-trajectory masking strategy together with a tool-use reward gain, and claims that these components allow the base VLM to acquire an effective tool-use policy through pure RL without any supervised fine-tuning data. A multi-agent system is used to construct a visually enriched multi-image QA dataset, and the authors report SOTA results on mainstream single- and multi-image benchmarks while providing an attention-based analysis of why tool use improves performance.

Significance. If the performance claims and the causal contribution of the masking and reward components are substantiated by rigorous ablations and attention measurements, the work would constitute a meaningful step toward training multi-image VLM agents with minimal supervised data. The explicit attention-perspective analysis and the release of code and data would be additional strengths.

major comments (3)
  1. [§4] §4 (Experiments) and associated tables: The central SOTA claim and the assertion that the two-layer masking plus tool-use reward gain enable pure-RL acquisition of tool-use policy rest on performance numbers, error bars, and ablation tables that are not visible in the provided sections. Without these, it is impossible to verify whether the reported gains are robust or whether the masking and reward components are load-bearing.
  2. [§3.2] §3.2 (Method, attention analysis): The claim that tool usage enhances performance “from an attention perspective” and that the two-layer masking prevents gradual neglect of visual tokens requires quantitative attention-shift metrics or visualizations before and after the masking strategy; the current text supplies only qualitative description.
  3. [§3.1–3.3] §3.1–3.3 (Reward design and masking): The tool-use reward gain and the two-layer motion-trajectory masking are presented as key enablers of credit assignment across tool calls and image tokens, yet no ablation isolating each component (e.g., performance with/without masking, with/without reward gain) is shown; such ablations are necessary to support the “eliminating the need for costly supervised fine-tuning data” claim.
minor comments (2)
  1. [Abstract] The abstract states that code and data “will be released soon”; a concrete release plan or repository link should be added before publication.
  2. [§3.2] Notation for the two-layer masking (e.g., which layers correspond to which trajectory segments) should be defined explicitly in §3.2 with a small diagram or pseudocode.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have revised the manuscript to provide the requested quantitative evidence, tables, and ablations.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments) and associated tables: The central SOTA claim and the assertion that the two-layer masking plus tool-use reward gain enable pure-RL acquisition of tool-use policy rest on performance numbers, error bars, and ablation tables that are not visible in the provided sections. Without these, it is impossible to verify whether the reported gains are robust or whether the masking and reward components are load-bearing.

    Authors: We acknowledge the need for clear visibility of the supporting data. The full manuscript contains the SOTA results with error bars (from three independent runs) and ablation tables in Section 4. To improve accessibility, we have added a consolidated main-text table summarizing key metrics and component contributions, ensuring the robustness of the reported gains is directly verifiable. revision: yes

  2. Referee: [§3.2] §3.2 (Method, attention analysis): The claim that tool usage enhances performance “from an attention perspective” and that the two-layer masking prevents gradual neglect of visual tokens requires quantitative attention-shift metrics or visualizations before and after the masking strategy; the current text supplies only qualitative description.

    Authors: We agree that quantitative metrics strengthen the analysis. The revised manuscript now includes attention-shift metrics (average visual-token attention weight over reasoning steps) and before/after attention map visualizations. These additions quantify how the masking strategy counters visual neglect and how tool use alters attention distribution. revision: yes

  3. Referee: [§3.1–3.3] §3.1–3.3 (Reward design and masking): The tool-use reward gain and the two-layer motion-trajectory masking are presented as key enablers of credit assignment across tool calls and image tokens, yet no ablation isolating each component (e.g., performance with/without masking, with/without reward gain) is shown; such ablations are necessary to support the “eliminating the need for costly supervised fine-tuning data” claim.

    Authors: We concur that isolating each component is essential. We have added a dedicated ablation table in the revised manuscript comparing variants (no masking, no reward gain, and full model). The results demonstrate that both the two-layer masking and tool-use reward gain are necessary for successful pure-RL tool-use policy learning, directly supporting the claim regarding elimination of supervised fine-tuning data. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical RL training with independent dataset and rewards

full rationale

The paper presents an empirical training procedure for a multi-image VLM agent using end-to-end RL, custom reflection/verification tools, two-layer motion trajectory masking, and a tool-use reward gain. No equations, derivations, or first-principles predictions are offered that reduce performance claims to fitted parameters or self-referential definitions. The dataset is constructed separately via a multi-agent system to address data gaps, and results are validated on external benchmarks. The central claim—that these components enable effective tool-use policy acquisition without SFT—rests on experimental outcomes rather than any self-definitional or fitted-input reduction, rendering the approach self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that VLMs neglect visual inputs over long generations and on the design choices of custom tools, masking strategy, and reward shaping whose precise parameterization and interaction effects are not detailed in the abstract.

free parameters (1)
  • tool-use reward gain
    Scaling factor in the RL reward for tool calls; value and tuning procedure not specified in abstract.
axioms (1)
  • domain assumption VLMs tend to gradually neglect visual inputs during inference
    Invoked to motivate the design of reflection and verification tools.
invented entities (1)
  • IMAgent no independent evidence
    purpose: End-to-end RL-trained multi-image visual agent
    The model resulting from the described training procedure.

pith-pipeline@v0.9.0 · 5556 in / 1393 out tokens · 49671 ms · 2026-05-17T00:54:54.901263+00:00 · methodology

discussion (0)

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