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arxiv: 2508.03556 · v3 · pith:4QW3B5Q3new · submitted 2025-08-05 · 💻 cs.LG

VRPRM: Process Reward Modeling via Visual Reasoning

Xinquan Chen , Chongying Yue , Bangwei Liu , Xuhong Wang , Yingchun Wang , Chaochao Lu This is my paper

Pith reviewed 2026-05-22 12:16 UTC · model grok-4.3

classification 💻 cs.LG
keywords Process Reward ModelVisual ReasoningChain-of-ThoughtSupervised Fine-TuningReinforcement LearningLarge Language ModelsBest-of-N SamplingData Efficiency
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The pith

A process reward model gains deep reasoning ability by injecting visual reasoning through a two-stage training pipeline.

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

The paper proposes VRPRM to address the lack of long-term reasoning in most process reward models and the high cost of annotating Chain-of-Thought data for them. It introduces a two-stage approach: supervised fine-tuning on a small set of 3.6K CoT-PRM examples to build visual reasoning, followed by reinforcement learning on 50K non-CoT PRM data. This combination allows VRPRM to exceed the performance of standard non-thinking PRMs trained on 400K examples total. A sympathetic reader would care because it promises higher-quality step-by-step evaluation of LLM outputs at much lower annotation expense, potentially making fine-grained reward modeling more practical across tasks.

Core claim

VRPRM is a process reward model that incorporates visual reasoning via an efficient two-stage training strategy. Using only 3.6K CoT-PRM Supervised Fine-Tuning data and 50K non-CoT PRM Reinforcement Learning training data, VRPRM surpasses non-thinking PRMs trained on a total of 400K data and delivers a relative performance improvement of up to 118% over the base model in Best-of-N experiments. This confirms that the combined training strategy achieves higher quality reasoning capabilities at lower data annotation cost.

What carries the argument

The two-stage training pipeline that first applies supervised fine-tuning on CoT-PRM data to inject visual reasoning and then uses reinforcement learning on non-CoT PRM data to refine the model.

If this is right

  • Process reward models can reach higher reasoning quality with far smaller annotated datasets.
  • The separation of visual-reasoning SFT from standard RL allows efficient scaling of PRM capabilities.
  • Annotation budgets for fine-grained LLM evaluation can be reduced while maintaining or improving step-level accuracy.
  • A new training paradigm emerges that combines limited CoT data with abundant non-CoT data for reward modeling.

Where Pith is reading between the lines

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

  • The same two-stage injection of visual reasoning might improve outcome reward models or other preference models without proportional data increases.
  • If visual reasoning transfers across domains, the method could lower costs for training reward models on math, code, or multimodal tasks.
  • Future work could test whether the visual-reasoning stage reduces specific error types such as step hallucination in long reasoning chains.

Load-bearing premise

Visual reasoning can be reliably added to a process reward model through this two-stage pipeline without creating new failure modes or needing extra task-specific visual data.

What would settle it

Train a non-thinking PRM on 400K examples and compare its Best-of-N performance against VRPRM trained on the claimed 3.6K plus 50K data; if VRPRM does not show at least an 80 percent relative gain over the base model, the central efficiency claim fails.

Figures

Figures reproduced from arXiv: 2508.03556 by Bangwei Liu, Chaochao Lu, Chongying Yue, Xinquan Chen, Xuhong Wang, Yingchun Wang.

Figure 1
Figure 1. Figure 1: Overall framework of VRPRM. We first use Claude-3.7-Sonnet to generate CoT-PRM data with long-horizon rea [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Best-of-N results of InternVL2.5-8B across four [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: , in CoT-PRM Data, more than 90% of the responses have a thought length of more than 1500 characters, which shows that CoT-PRM Data has good response quality and is a high-quality long-range reasoning process label dataset. The step distribution statistics of CoT-PRM Data are shown in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of Step Count Multimodal Reasoning Benchmarks We selected five multimodal reasoning benchmarks: MathVista (Lu et al. 2024) is a benchmark specifically designed to evaluate the capabilities of Multimodal Large Language Models (MLLMs) in visual mathematical reason￾ing. The dataset contains 6,141 examples, sourced from 28 existing multimodal math-related datasets, along with three newly created s… view at source ↗
Figure 5
Figure 5. Figure 5: Prompt for Synthetic CoT-PRM Data [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An Example of CoT-PRM Data [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An Example of VRPRM Output [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) because it can perform fine-grained evaluation of the reasoning steps of generated content. However, most PRMs lack long-term reasoning and deep thinking capabilities. On the other hand, although a few works have tried to introduce Chain-of-Thought (CoT) capability into PRMs, the annotation cost of CoT-PRM data is too expensive to play a stable role in various tasks. To address the above challenges, we propose VRPRM, a process reward model via visual reasoning, and design an efficient two-stage training strategy. Experimental results show that using only 3.6K CoT-PRM Supervised Fine-Tuning(SFT) data and 50K non-CoT PRM Reinforcement Learning (RL) training data, VRPRM can surpass the non-thinking PRM with a total data volume of 400K and achieved a relative performance improvement of up to 118\% over the base model in the BoN experiment. This result confirms that the proposed combined training strategy can achieve higher quality reasoning capabilities at a lower data annotation cost, thus providing a new paradigm for PRM training with more efficient data utilization.

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 / 2 minor

Summary. The paper introduces VRPRM, a process reward model that incorporates visual reasoning to improve long-term and deep thinking in step-wise evaluation of LLM outputs. It proposes a two-stage training pipeline consisting of supervised fine-tuning on 3.6K CoT-PRM data followed by reinforcement learning on 50K non-CoT PRM data, claiming this enables VRPRM to surpass non-thinking PRMs trained on a total of 400K data while delivering up to 118% relative improvement over the base model in Best-of-N (BoN) experiments.

Significance. If the empirical results hold after proper verification, the work would be significant for demonstrating a data-efficient paradigm for PRM training that reduces expensive CoT annotation costs by leveraging visual reasoning transfer. This could meaningfully advance post-training techniques for LLMs by achieving higher-quality reasoning capabilities with substantially lower data volumes.

major comments (2)
  1. Abstract: the headline claim that 3.6K CoT-PRM SFT + 50K non-CoT RL data surpasses a 400K non-thinking PRM with up to 118% relative BoN gain is presented without any details on visual reasoning implementation, exact baseline models and training procedures, statistical significance, or ablations isolating the visual component; this prevents evaluation of the central performance claim.
  2. Two-stage pipeline (described in abstract and training sections): the assumption that visual reasoning reliably transfers from the small SFT set to the larger RL set without introducing new failure modes (e.g., hallucinated diagrams or modality misalignment) is unverified by ablations or cross-task generalization tests, which is load-bearing for the claimed annotation savings.
minor comments (2)
  1. Provide explicit description of how visual elements are generated, referenced, or rendered within the CoT-PRM data and how they are fused into the PRM architecture.
  2. Include tables reporting exact baseline configurations, dataset compositions, and ablation results for the visual reasoning component.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. We address each major comment below with clarifications from the manuscript and indicate where revisions have been made to improve transparency and rigor.

read point-by-point responses

  1. Referee: Abstract: the headline claim that 3.6K CoT-PRM SFT + 50K non-CoT RL data surpasses a 400K non-thinking PRM with up to 118% relative BoN gain is presented without any details on visual reasoning implementation, exact baseline models and training procedures, statistical significance, or ablations isolating the visual component; this prevents evaluation of the central performance claim.

    Authors: We agree that the abstract, as a concise summary, does not include implementation specifics. These are provided in the main text: visual reasoning is implemented via diagram generation and interpretation in Section 3, baseline models and training details (including the 400K non-thinking PRM) are in Section 4, statistical significance via multiple runs with standard deviations appears in Section 5, and ablations isolating the visual component are in Section 5.3. To address the concern, we have revised the abstract to briefly reference the two-stage pipeline and direct readers to the relevant sections for full experimental context. revision: yes

  2. Referee: Two-stage pipeline (described in abstract and training sections): the assumption that visual reasoning reliably transfers from the small SFT set to the larger RL set without introducing new failure modes (e.g., hallucinated diagrams or modality misalignment) is unverified by ablations or cross-task generalization tests, which is load-bearing for the claimed annotation savings.

    Authors: We acknowledge the importance of verifying transfer and failure modes. The manuscript reports ablations in Section 5.2 that compare the full two-stage VRPRM against SFT-only and RL-only variants, showing performance gains attributable to visual reasoning transfer. Cross-task results on math, coding, and science benchmarks are presented in Table 3. Manual analysis of outputs (Section 5.4) indicates low incidence of hallucinated diagrams. We have added explicit discussion of modality alignment checks and additional failure-mode analysis to the revised version to further substantiate the annotation savings claim. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical results on external benchmarks.

full rationale

The paper describes an empirical two-stage training pipeline for VRPRM using 3.6K CoT-PRM SFT data followed by 50K non-CoT RL data, with performance claims supported by direct comparisons to baselines on external benchmarks such as BoN. No mathematical derivation chain, equations, or fitted parameters are presented that reduce to the inputs by construction. The central result is a reported empirical improvement rather than a self-referential prediction or self-citation load-bearing premise. The work is therefore self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted. The central claim rests on the unstated assumption that visual reasoning transfers usefully to text-only reward modeling.

pith-pipeline@v0.9.0 · 5762 in / 1026 out tokens · 34744 ms · 2026-05-22T12:16:09.815003+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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

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

    cs.CL 2026-05 unverdicted novelty 6.0

    PDCR improves vision-language reasoning by computing separate normalized confidence advantages for perception steps and reasoning steps after unsupervised decomposition.

Reference graph

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