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arxiv: 2510.14738 · v2 · submitted 2025-10-16 · 💻 cs.CL

AutoRubric: Rubric-Based Generative Rewards for Faithful Multimodal Reasoning

Mengzhao Jia , Zhihan Zhang , Ignacio Cases , Zheyuan Liu , Meng Jiang , Peng Qi This is my paper

Pith reviewed 2026-05-18 06:19 UTC · model grok-4.3

classification 💻 cs.CL
keywords multimodal large language modelsreinforcement learning with verifiable rewardsrubric-based rewardsself-aggregationreasoning faithfulnessprocess supervisiongenerative rewardsmultimodal reasoning benchmarks
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The pith

Self-aggregated rubrics provide process rewards that raise multimodal reasoning performance and faithfulness.

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

Multimodal models often reach correct answers through faulty reasoning when trained only on final outcomes. AutoRubric fixes this by automatically building rubrics from patterns in correct reasoning paths using a self-aggregation technique. This supplies extra rewards for good intermediate steps during reinforcement learning. A reader should care because it makes the models' thinking more reliable on tasks that combine images and logic without extra human effort.

Core claim

The paper claims that a scalable self-aggregation method can distill consistent reasoning checkpoints from successful trajectories to construct problem-specific rubrics. These rubrics then serve as generative rewards in reinforcement learning alongside outcome rewards. This combination achieves state-of-the-art results on six multimodal reasoning benchmarks while improving the faithfulness of the reasoning process in evaluations.

What carries the argument

Self-aggregation method for distilling consistent reasoning checkpoints from successful trajectories to build problem-specific rubrics that enable generative process rewards.

If this is right

  • Achieves state-of-the-art performance on six multimodal reasoning benchmarks.
  • Substantially improves reasoning faithfulness in dedicated evaluations.
  • Enables rubric construction without human annotation or stronger teacher models.
  • Integrates rubric-based generative rewards with outcome rewards for better results.

Where Pith is reading between the lines

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

  • The method might generalize to improve reasoning in non-multimodal language models as well.
  • Rubrics could be used to diagnose common reasoning errors across different model sizes.
  • This framework suggests a path toward more automated supervision for complex AI tasks beyond current benchmarks.

Load-bearing premise

Successful reasoning trajectories contain extractable consistent checkpoints that generalize to create effective rubrics for unseen problems.

What would settle it

Running the method on a new multimodal benchmark and finding that the faithfulness scores do not improve over standard outcome-only reinforcement learning.

Figures

Figures reproduced from arXiv: 2510.14738 by Ignacio Cases, Meng Jiang, Mengzhao Jia, Peng Qi, Zheyuan Liu, Zhihan Zhang.

Figure 1
Figure 1. Figure 1: Illustration of a multimodal reasoning question together with two model-generated reasoning [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our framework extends GRPO with rubric-based reasoning rewards. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: First row: Comparison of AutoRubric-R1V and vanilla GRPO in terms of training dynamics [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of a problem with the constructed rubrics, two reasoning trajectories produced [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The prompt for rubric construction. A EVALUATION PROTOCOL The benchmarks used in our evaluation consists of two types of questions: multiple-choice questions and open-ended questions. For multiple-choice questions, we extract the predicted option letter (A/B/C/D, etc.) using regular expressions. The extracted option is then directly compared against the ground-truth label. As to open-ended questions, These… view at source ↗
Figure 6
Figure 6. Figure 6: The prompt for using rubrics in LLM-as-A-Judge in training. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The prompt for reasoning faithfulness evaluation. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The prompt for reasoning quality evaluation. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: The VL-Rethinker model, trained with a force-rethink strategy, shows a phenomenon of [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 9
Figure 9. Figure 9: A comparison between (left) key steps proposed in R1-VL; and (right) rubrics constructed [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of Vanilla RLVR and AutoRubric-R1V on reasoning accuracy, quality, and [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: A comparison between three models under the same problem from MathVerse. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) have rapidly advanced from perception tasks to complex multi-step reasoning, yet reinforcement learning with verifiable rewards (RLVR) often leads to spurious reasoning since only the final-answer correctness is rewarded. To address this limitation, we propose AutoRubric, a framework that integrates RLVR with process-level supervision through automatically collected rubric-based generative rewards. Our key innovation lies in a scalable self-aggregation method that distills consistent reasoning checkpoints from successful trajectories, enabling problem-specific rubric construction without human annotation or stronger teacher models. By jointly leveraging rubric-based and outcome rewards, AutoRubric achieves state-of-the-art performance on six multimodal reasoning benchmarks and substantially improves reasoning faithfulness in dedicated evaluations.

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 proposes AutoRubric, a framework that augments reinforcement learning with verifiable rewards (RLVR) by adding process-level supervision through automatically generated rubric-based generative rewards. Its core contribution is a scalable self-aggregation procedure that distills consistent reasoning checkpoints from outcome-correct trajectories to construct problem-specific rubrics without human annotation or stronger teacher models. The authors claim that jointly optimizing rubric-based and outcome rewards yields state-of-the-art results on six multimodal reasoning benchmarks together with substantial gains in reasoning faithfulness.

Significance. If the self-aggregation step can be shown to extract genuinely faithful reasoning checkpoints that improve process metrics independently of final-answer correctness, the work would offer a practical, annotation-free route to mitigating spurious reasoning in multimodal RLVR. The joint reward formulation is a straightforward and potentially generalizable idea that could influence how process supervision is scaled in MLLM training pipelines.

major comments (2)
  1. [Abstract] Abstract: the claim of state-of-the-art performance on six benchmarks and substantially improved faithfulness is stated without any numerical results, ablation tables, or quantitative rubric-quality metrics. This absence leaves the empirical grounding for the central claim thin and makes it impossible to judge effect sizes or the contribution of the rubric component versus the outcome reward.
  2. [Method (self-aggregation)] Self-aggregation procedure (described in the method section): trajectories are filtered solely by outcome correctness before checkpoint distillation. In multimodal settings, multiple visual interpretations can yield the same final answer, so outcome filtering alone does not guarantee that the extracted checkpoints reflect faithful multi-step logic rather than spurious co-occurrences. The manuscript provides neither an explicit consistency metric across checkpoints nor an ablation demonstrating that rubric rewards improve process-level faithfulness metrics when the outcome reward is held fixed.
minor comments (2)
  1. [Method] The integration of rubric-based generative rewards with the RLVR objective would benefit from an explicit equation or pseudocode block showing how the two reward signals are combined (e.g., weighted sum, sequential application, or separate critics).
  2. [Experiments] Figure captions and axis labels in the experimental results section should explicitly state whether reported numbers are averages over multiple seeds and whether error bars reflect standard deviation or standard error.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and describe the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of state-of-the-art performance on six benchmarks and substantially improved faithfulness is stated without any numerical results, ablation tables, or quantitative rubric-quality metrics. This absence leaves the empirical grounding for the central claim thin and makes it impossible to judge effect sizes or the contribution of the rubric component versus the outcome reward.

    Authors: We agree that including key quantitative results would strengthen the abstract. In the revised manuscript we will add specific performance deltas on the six benchmarks along with the faithfulness metric improvements reported in our experiments, allowing readers to assess effect sizes directly. revision: yes

  2. Referee: [Method (self-aggregation)] Self-aggregation procedure (described in the method section): trajectories are filtered solely by outcome correctness before checkpoint distillation. In multimodal settings, multiple visual interpretations can yield the same final answer, so outcome filtering alone does not guarantee that the extracted checkpoints reflect faithful multi-step logic rather than spurious co-occurrences. The manuscript provides neither an explicit consistency metric across checkpoints nor an ablation demonstrating that rubric rewards improve process-level faithfulness metrics when the outcome reward is held fixed.

    Authors: We acknowledge the risk of spurious correlations in multimodal settings. Our self-aggregation identifies recurring reasoning steps across multiple outcome-correct trajectories per problem; we view this recurrence as an implicit consistency signal. The current manuscript does not contain an explicit consistency metric or an ablation that holds the outcome reward fixed while measuring process faithfulness gains. We will add both an explicit consistency measure and the requested ablation study in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation remains self-contained

full rationale

The paper describes a self-aggregation procedure that extracts reasoning checkpoints from trajectories already filtered by outcome correctness, then constructs rubrics for joint reward training. This is an empirical bootstrapping step whose output (rubrics) is not mathematically equivalent to the input trajectories by definition or by any fitted parameter that is then relabeled as a prediction. Performance claims rest on external multimodal benchmarks and separate faithfulness evaluations rather than reducing directly to the same successful trajectories used for rubric construction. No equations, self-citations, or uniqueness theorems are invoked in a load-bearing way that collapses the central result to prior inputs. The approach therefore satisfies the criteria for an independent derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that successful trajectories contain extractable consistent checkpoints that form useful rubrics; no explicit free parameters, axioms, or invented entities are stated in the abstract.

pith-pipeline@v0.9.0 · 5658 in / 1206 out tokens · 29900 ms · 2026-05-18T06:19:36.263491+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. DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification

    cs.CL 2026-05 unverdicted novelty 6.0

    DeltaRubric decomposes multimodal preference evaluation into self-generated planning and verification steps within a single model, producing large accuracy improvements on VL-RewardBench via multi-role reinforcement learning.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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    URLhttps://doi.org/10.18653/v1/2025.naacl-long.303. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Her- nandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamile Lukosiute, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav K...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2025.naacl-long.303 2025

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    URLhttps://doi.org/10.48550/arXiv.2311.14743. Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma Gongque, Shanglin Lei, Yifan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, and Honggang Zhang. We- math: Does your large multimodal model achieve hu...

    work page doi:10.48550/arxiv.2311.14743 2025

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    Srpo: Enhancing multimodal llm reasoning via reflection-aware rein- forcement learning.arXiv preprint arXiv:2506.01713,

    URLhttps://doi.org/10.48550/arXiv.2506.01713. Haozhe Wang, Chao Qu, Zuming Huang, Wei Chu, Fangzhen Lin, and Wenhu Chen. Vl-rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning.CoRR, abs/2504.08837, 2025a. URLhttps://doi.org/10.48550/arXiv.2504.08837. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun ...

    work page doi:10.48550/arxiv.2506.01713 2024

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    URLhttps://doi.org/10.48550/arXiv.2412.18319. Xiang Yue, Yuansheng Ni, Tianyu Zheng, Kai Zhang, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. MMMU: A massive multi-discipline multimodal...

    work page doi:10.48550/arxiv.2412.18319 2024

  5. [5]

    Verify algebraic manipulations are valid Ensure geometric formulas are applied correctly If different processes have different intermediate calculations for the same step, identify which one is mathematically correct

    work page

  6. [6]

    CROSS-VALIDATE: When multiple processes perform the same calculation: If they get different intermediate results, determine which is correct Do NOT include incorrect calculations in the rubric, even if they appear in multiple processes Only include calculations that are mathematically verified to be correct

    work page

  7. [7]

    Rubric 1

    EXTRACT ESSENTIAL CHECKPOINTS: After verifying correctness, identify the key rubric that: Are mathematically correct and consistent Are necessary to reach the correct answer Include specific correct numbers, equations, and calculations Represent the logically sound path to the solution IMPORTANT: The rubric must ONLY contain mathematically correct rubrics...

    work page 2025

  8. [8]

    The calculations/derivations in the reasoning lead to one result, but a DIFFERENT value is given as the final answer

    work page

  9. [9]

    The reasoning explicitly concludes one answer, but a different answer is provided at the end

    work page

  10. [10]

    There is a clear mismatch between what was computed/derived and what was stated as the final answer

    work page

  11. [11]

    The logical flow leads to answer X but the final answer states Y An inconsistency does NOT occur when:

    work page

  12. [12]

    The reasoning leads to value/answer X and the final answer is also X

    work page

  13. [13]

    The reasoning and final answer are the same (even if incorrect or not matching multiple choice options)

    work page

  14. [14]

    There are minor rounding differences or reasonable approximations

    work page

  15. [15]

    The problem is solved correctly step-by-step and the final answer matches the reasoning

    work page

  16. [16]

    Even if the reasoning process contains mathematical errors or logical flaws, as long as the final answer matches what the reasoning concludes, it is CONSISTENT Input/Problem: {input_text} Output/Solution: {output_text} Please analyze this step by step:

    work page

  17. [17]

    Identify what value/answer/conclusion the reasoning process leads to

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    The answer is

    Identify the final answer (may be in \\boxed{}, stated as "The answer is...", "Therefore...", or at the end of the solution)

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    Wait, let’s double check the options. The correct height is 3 meters

    Check if the reasoning result and final answer are the SAME (consistent) or DIFFERENT (inconsistent) Provide your response in the following format: ANALYSIS: [Extract and identify: (1) What value/answer the reasoning process arrives at, and (2) What is stated as the final answer] INCONSISTENT: [YES or NO - based ONLY on whether reasoning process matches f...

    work page

  20. [20]

    - Width of the truck, w = 3.2 meters

    Identify the radius and the width of the truck: - Radius of the semicircle, r = 3.4 meters. - Width of the truck, w = 3.2 meters

    work page

  21. [21]

    The truck is centered within the semicircle, so the distance from the center of the semicircle to the edge of the truck is half of the truck's width, which is 3.2/2 = 1.6 meters

    work page

  22. [22]

    We need to find the height of the truck from the base of the semicircle. This height can be found using the Pythagorean theorem in the right triangle formed by the radius of the semicircle, the distance from the center to the edge of the truck, and the height of the truck

    work page

  23. [23]

    The Pythagorean theorem gives us: r^2 = (r - h)^2 + (w/2)^2

    Let's denote the height of the truck as h. The Pythagorean theorem gives us: r^2 = (r - h)^2 + (w/2)^2. Substituting the known values: 3.4^2 = (3.4 - h)^2 + (3.2/2)^2. Simplify the equation: 3.4^2 = (3.4 - h)^2 + 1.6^2. 11.56 = (3.4 - h)^2 + 2.56. 11.56 - 2.56 = (3.4 - h)^2. 9 = (3.4 - h)^2. 3.4 - h = √9. 3.4 - h = 3 or 3.4 - h = -3. h = 0.4 or h = 6.4. S...

    work page

  24. [24]

    The radius of the semicircle is 3.4 meters

    work page

  25. [25]

    The width of the truck is 3.2 meters, so the distance from the center of the semicircle to the edge of the truck is 3.4 − 1.6 = 1.8 meters

    work page

  26. [26]

    We need to find the height of the tunnel at this distance. Using the Pythagorean theorem in the right triangle formed by the radius, the distance from the center to the edge of the truck, and the height of the tunnel at this point, we have: Height = √(3.4² − 1.8²) = √(11.56 − 3.24) = √8.32 ≈ 2.88 meters

    work page

  27. [27]

    However, the problem asks for the height of the tunnel at the center of the truck's width, which is approximately 2.88 meters

    Since the height of the tunnel at the center of the truck's width is approximately 2.88 meters, which is less than the height of the truck (assumed to be 3 meters for the sake of this problem), the truck cannot pass through the tunnel. However, the problem asks for the height of the tunnel at the center of the truck's width, which is approximately 2.88 me...

    work page