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arxiv: 2606.17678 · v1 · pith:MWIPRXHBnew · submitted 2026-06-16 · 💻 cs.CV · cs.AI

See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL

Yilian Liu , Sicong Leng , Guoshun Nan , Junyi Zhu , Jiayu Huang , Minghao Sun , Xuancheng Zhu , Yisong Chen
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Zexian Wei Xiaofeng Tao
This is my paper

Pith reviewed 2026-06-27 01:41 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords multimodal large language modelsvisual groundingreinforcement learningvisual evidenceGRPOpre-alignmentimage understandingsufficiency objective
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The pith

An intermediate RL stage called VEPA trains multimodal models to generate sufficient question-conditioned visual evidence descriptions before standard post-training.

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

The paper introduces Visual Evidence Pre-Alignment as a stage between caption pretraining and supervised fine-tuning or reinforcement learning. It applies a sufficiency-driven objective with Group Relative Policy Optimization to make models produce visual descriptions that contain enough detail to answer the question. Experiments show consistent gains on visually demanding benchmarks, and the authors attribute the improvement to stronger, transferable visual grounding rather than task-specific practice. A sympathetic reader cares because current multimodal models often produce answers that ignore or contradict image details despite strong text reasoning.

Core claim

Inserting VEPA between pretraining and post-training, where models optimize question-conditioned visual evidence descriptions under a sufficiency-driven GRPO objective, produces strengthened visual grounding that transfers to downstream tasks, raises accuracy on visually demanding evaluations, and works together with standard supervised post-training without requiring additional task-specific training.

What carries the argument

Sufficiency-driven Group Relative Policy Optimization (GRPO) applied to question-conditioned visual evidence descriptions inside the VEPA intermediate stage.

If this is right

  • Performance rises on visually demanding evaluations across diverse benchmarks.
  • Gains originate from improved transferable visual grounding rather than added task-specific training.
  • VEPA can be inserted into existing pipelines and complements supervised fine-tuning plus reinforcement learning post-training.
  • The method requires no extra task-specific training data to produce the observed improvements.

Where Pith is reading between the lines

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

  • Training pipelines could separate visual alignment from reasoning alignment to improve efficiency.
  • Similar sufficiency objectives might be defined for other input types such as video or audio to strengthen grounding before reasoning stages.
  • Pre-alignment may reduce the amount of later supervision needed for tasks that depend on fine visual details.

Load-bearing premise

That optimizing for sufficiency of visual evidence descriptions in the intermediate stage will create grounding improvements that transfer to final tasks instead of being tied to the specific benchmarks or reward signals used during that stage.

What would settle it

Train models with VEPA on one set of visually demanding tasks, then evaluate on a separate collection of image-based questions that differ in visual content and format; if accuracy shows no consistent lift over a matched baseline without VEPA, the transfer claim is false.

Figures

Figures reproduced from arXiv: 2606.17678 by Guoshun Nan, Jiayu Huang, Junyi Zhu, Minghao Sun, Sicong Leng, Xiaofeng Tao, Xuancheng Zhu, Yilian Liu, Yisong Chen, Zexian Wei.

Figure 1
Figure 1. Figure 1: Motivation and overview. The standard two-stage recipe often yields coarse alignment and encourages shortcut an￾swering that ignores visual details. We insert VEPA as an interme￾diate stage that trains the model to produce question-conditioned visual evidence using sufficiency-driven GRPO, with a frozen blind reader (an LLM) answering and verifying whether the evi￾dence suffices to recover the answer. This… view at source ↗
Figure 2
Figure 2. Figure 2: Framework of VEPA. VEPA is inserted between pretraining and post-training. Given an image v and a question q, the policy MLLM πθ is prompted to generate question-conditioned visual evidence and samples a group of candidate visual evidence {e}. A frozen text-only blind reader (auxiliary LLM) answers using only (q, e). We optimize πθ with a novel sufficiency-driven objective via GRPO. Overall, the proposed V… view at source ↗
Figure 3
Figure 3. Figure 3: Performance evolution during VEPA. (A) illustrates the Accuracy trends on the MME dataset, while (B) presents the corresponding results on the MMStar dataset. together, these results indicate that VEPA selectively in￾creases reliance on visual input when vision is essential, without degrading robustness in broader VQA settings. Insight III. Counterfactual image corruption reveals that VEPA increases relian… view at source ↗
Figure 5
Figure 5. Figure 5: Detailed accuracy comparison on the POPE dataset across three categories: Adversarial, Random, and Popular. (A) displays results for Qwen2.5-7B-Instruct, while (B) shows GPT-5 Nano. A.3. More Experiment Results on Evidence Quality This section reports additional evidence-quality results complementing §5.3. Beyond the aggregate scores, we further decompose POPE into its three subsets (Adversarial, Random, P… view at source ↗
Figure 6
Figure 6. Figure 6: Fine-grained performance breakdown on MME. We compare the baseline SFT against VEPA+SFT across various sub-tasks. The results demonstrate that VEPA provides consistent improvements across most categories, particularly in tasks requiring precise visual grounding. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Fine-grained performance breakdown on MMStar. We compare the baseline SFT (blue) against VEPA+SFT (green) across various sub-tasks. The results demonstrate that VEPA provides consistent improvements across most categories, particularly in tasks requiring precise visual grounding. Answer based on the image: How many push pins are there? Base Based on the image, there are 8 push pins visible. They are scatte… view at source ↗
Figure 8
Figure 8. Figure 8: Case 1 on Counting Answer based on the image: How many people are in the photograph? Base The photograph shows three people, two of whom are in green and white jerseys and one in a red jersey. VEPA There are four people in the photograph. They are the three men in the foreground, and one person in the background [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Case 3 on Counting and Attribute Answer based on the image: what color is the train? Base Based on the image, the color of the train is not specified. It is only given that there is a train with a number on it...... VEPA Based on the image, the train is colored in a tan or yellowish-brown color. The specific shade of tan or brown is not clearly visible, but the color is similar to what you would typically… view at source ↗
Figure 12
Figure 12. Figure 12: Case 5 on Comparison Answer based on the image: How many guide signs are there in total in the picture? Base The sign pointing to the right says \"Norderstedt\" and \"Langenhorn\". There are a total of 2 guide signs in the picture. VEPA The place name written on the sign pointing to the right is \"Norderstedt\". There are a total of 5 guide signs in the picture [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Evaluation Metrics of Auxiliary Model Training. The figure presents the evolution of the mean response length (y-axis) over training steps (x-axis). The blue and green lines correspond to the two distinct auxiliary model settings evaluated in this experiment. For each setting, the solid darker lines indicate the smoothed values (exponential moving average, weight 0.85), while the faint background lines re… view at source ↗
Figure 15
Figure 15. Figure 15: Reward Score Trend During Training (5k-VEPA-3b-aux-critic). 0 50 100 150 200 Step 0.3 0.4 0.5 0.6 0.7 Reward Score Reward Score Trend During Training Original Smoothed [PITH_FULL_IMAGE:figures/full_fig_p020_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Reward Score Trend During Training (5k-VEPA-7b-aux-critic). 20 [PITH_FULL_IMAGE:figures/full_fig_p020_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Reward Score Trend During Training (10k-VEPA-7b-aux-critic) [PITH_FULL_IMAGE:figures/full_fig_p021_17.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference. The prevailing training paradigm relies on large-scale caption-based pretraining for general alignment, followed by supervised fine-tuning and reinforcement learning to enable instruction following and complex reasoning. However, such pretraining provides only weak visual grounding: short, coarse captions bias models toward salient objects while neglecting fine-grained visual evidence. In this paper, we introduce Visual Evidence Pre-Alignment (VEPA), an intermediate stage between pretraining and post-training that explores a novel sufficiency-driven objective with Group Relative Policy Optimization (GRPO) to optimize question-conditioned visual evidence descriptions. Extensive experiments across diverse benchmarks show that our VEPA consistently enhances performance on visually demanding evaluations and complements standard supervised post-training. Further analyses show that the income stems from strengthened, transferable visual grounding, rather than from additional task-specific training.

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 / 1 minor

Summary. The paper proposes Visual Evidence Pre-Alignment (VEPA), an intermediate training stage for multimodal LLMs placed between caption-based pretraining and standard supervised post-training. VEPA uses a sufficiency-driven objective optimized via Group Relative Policy Optimization (GRPO) on question-conditioned visual evidence descriptions, with the central claim that this produces strengthened, transferable visual grounding that yields consistent gains on visually demanding benchmarks and is not reducible to task-specific artifacts.

Significance. If the transferability claim holds under rigorous controls, the work would offer a concrete, modular addition to existing MLLM pipelines that targets a documented weakness (coarse caption pretraining) without requiring changes to the final supervised or RL stages. The use of GRPO on a sufficiency objective is a clear technical choice that could be adopted or ablated by others.

major comments (2)
  1. [Abstract] Abstract: the claim that VEPA 'consistently enhances performance' and that 'the gain stems from strengthened, transferable visual grounding, rather than from additional task-specific training' is asserted without any reported numbers, baselines, ablation tables, or statistical tests. The full experimental section (including reward definition, training details, and cross-benchmark controls) is required to determine whether the data actually support the transferability conclusion.
  2. [Abstract / implied experimental design] The weakest assumption—that sufficiency-driven GRPO on question-conditioned descriptions produces grounding improvements that generalize beyond the intermediate-stage benchmarks and reward formulation—remains untested in the provided text. Explicit held-out-task or cross-domain evaluations (distinct from any benchmarks used to shape the GRPO reward) are needed to rule out benchmark-specific artifacts.
minor comments (1)
  1. [Abstract] Abstract contains the apparent typo 'the income stems from' (should be 'the gain stems from').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, clarifying the experimental support in the full manuscript while noting where revisions can strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that VEPA 'consistently enhances performance' and that 'the gain stems from strengthened, transferable visual grounding, rather than from additional task-specific training' is asserted without any reported numbers, baselines, ablation tables, or statistical tests. The full experimental section (including reward definition, training details, and cross-benchmark controls) is required to determine whether the data actually support the transferability conclusion.

    Authors: The abstract follows standard conventions by summarizing claims at a high level. The full manuscript contains the complete experimental section with reward definitions, training details, baseline comparisons, ablation tables, and statistical analyses across benchmarks that substantiate the performance gains and the conclusion that improvements derive from transferable visual grounding rather than task-specific artifacts. We will revise the abstract to incorporate key quantitative results where length permits. revision: partial

  2. Referee: [Abstract / implied experimental design] The weakest assumption—that sufficiency-driven GRPO on question-conditioned descriptions produces grounding improvements that generalize beyond the intermediate-stage benchmarks and reward formulation—remains untested in the provided text. Explicit held-out-task or cross-domain evaluations (distinct from any benchmarks used to shape the GRPO reward) are needed to rule out benchmark-specific artifacts.

    Authors: The manuscript evaluates VEPA on multiple visually demanding benchmarks that are distinct from the question-conditioned descriptions used to define the sufficiency objective and GRPO reward. Additional analyses explicitly test and confirm that gains transfer beyond the intermediate stage and are not reducible to artifacts of the reward formulation or task-specific training. We maintain that the current cross-benchmark design already addresses generalization; however, we can add explicit discussion clarifying the separation between reward data and evaluation sets. revision: no

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained empirical claim

full rationale

The paper introduces VEPA as an intermediate training stage using a sufficiency-driven GRPO objective to optimize question-conditioned visual evidence descriptions. Claims rest on experimental results showing performance gains on visually demanding benchmarks, attributed to strengthened transferable visual grounding. No equations, derivations, or load-bearing steps are present in the provided text that reduce any 'prediction' or result to fitted inputs, self-definitions, or self-citation chains by construction. The central mechanism is framed as an empirical outcome independent of the training signal, consistent with external benchmark evaluation rather than internal tautology. This is the expected non-finding for an applied RL method paper without mathematical derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract alone; no explicit free parameters, axioms, or invented entities are stated. The central premise that coarse captions are the primary source of weak visual grounding is treated as a domain assumption.

axioms (1)
  • domain assumption Coarse, short captions in pretraining bias models toward salient objects and neglect fine-grained visual evidence.
    Stated in the abstract as the motivation for inserting VEPA; if false, the need for the new stage is reduced.

pith-pipeline@v0.9.1-grok · 5731 in / 1250 out tokens · 32419 ms · 2026-06-27T01:41:26.054816+00:00 · methodology

discussion (0)

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Reference graph

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