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arxiv: 2511.11113 · v2 · submitted 2025-11-14 · 💻 cs.CV · cs.AI· cs.LG

VIDEOP2R: Video Understanding from Perception to Reasoning

Yifan Jiang , Yueying Wang , Rui Zhao , Toufiq Parag , Zhimin Chen , Zhenyu Liao , Jayakrishnan Unnikrishnan This is my paper

Pith reviewed 2026-05-17 22:21 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords video reasoningreinforcement fine-tuningchain of thoughtlarge video language modelsperceptionreasoningpolicy optimizationvideo benchmarks
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The pith

VideoP2R shows that separating perception and reasoning processes in training large video models leads to superior performance on reasoning benchmarks.

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

The paper introduces VideoP2R, a framework that applies reinforcement fine-tuning to large video language models by explicitly modeling perception and reasoning as separate steps. It generates a special chain-of-thought dataset that captures both processes and uses a modified optimization method that rewards each process independently. If this approach works as claimed, video models could better handle complex tasks like understanding events in videos and drawing conclusions from them. This matters because current video AI often mixes up seeing details with logical thinking, leading to errors in real-world applications such as surveillance or content analysis. The results indicate that perception outputs alone can support accurate reasoning without additional steps.

Core claim

The central claim is that by treating perception and reasoning as distinct processes in a two-stage reinforcement fine-tuning setup, video language models can achieve better understanding and reasoning. Specifically, a three-step pipeline creates a 162K dataset of process-aware chain-of-thought examples, and a process-aware group relative policy optimization algorithm assigns separate rewards to perception and reasoning phases. This yields state-of-the-art results on six of seven video benchmarks and confirms that perception outputs contain enough information for downstream reasoning.

What carries the argument

The key machinery is the process-aware group relative policy optimization (PA-GRPO) that provides separate rewards for perception and reasoning, supported by a generated process-aware chain-of-thought dataset from a three-step pipeline.

Load-bearing premise

That supplying separate rewards for perception and reasoning in PA-GRPO, combined with the generated process-aware CoT data, produces genuine improvements rather than artifacts of reward design or data generation choices.

What would settle it

A direct comparison where the same model is trained with standard group relative policy optimization without separate perception and reasoning rewards, and it matches or exceeds the reported performance, would challenge the necessity of the process-aware approach.

Figures

Figures reproduced from arXiv: 2511.11113 by Jayakrishnan Unnikrishnan, Rui Zhao, Toufiq Parag, Yifan Jiang, Yueying Wang, Zhenyu Liao, Zhimin Chen.

Figure 1
Figure 1. Figure 1: Comparison between GRPO-based video RFT framework (process-agnostic) and V [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of overall VIDEOP2R RFT framework (left) and the three-step CoT generation pipeline (right). 56, 71]. Time-R1 uses timestamp-aware and template rewards [56]; Video-R1 and STAR-R1 reward sensitiv￾ity to correct temporal order [14, 28]; Videochat-R1 and VersaVid-R1 adopt task-specific rewards [7, 26]; Vide￾oRFT adds a stage-aware semantic reward [52]. How￾ever, most prior efforts model video rea… view at source ↗
Figure 3
Figure 3. Figure 3: The illustration of the PA-GRPO algorithm. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of perception on downstream reasoning [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Success (Left) and Failure (Right) case of V [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Training Dynamics and Think-Answer Mismatch [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt Template for Observation Sufficiency Veri [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt Template for Process-aware CoT Generation. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 10
Figure 10. Figure 10: Word length (Left) and Word cloud (Right) Visual [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: An Annotation Example of the Video QA Sample [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 9
Figure 9. Figure 9: Embeddings visualization of VIDEOP2R-CoT-162K 7.5. Annotation Examples We provide annotation examples in Figs. 11 and 12 to il￾lustrate how our annotations explicitly separate percep￾tion from reasoning [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt Template for Answer Extraction. 10.3. Examples of Qwen Inference Output We present examples of Qwen’s outputs under differ￾ent configurations in our perception examination experi￾ment in [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt Template for Perception Examination Ex [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: RL training Dynamics of VIDEOP2R model consistently adheres to the process-aware infer￾ence template and maintains stable format compliance. Since the length reward is conditioned on both accuracy and format rewards, we instead visualize the lengths of the perception and reasoning segments during RL. We observe an initial increase followed by a decrease in both segments, indicating that the model adaptive… view at source ↗
Figure 16
Figure 16. Figure 16: Example of Think-Answer Mismatch. consistent with its original design, while VersaVid-R1 has too few available traces on VSI-Bench for meaning￾ful statistics. All results are computed on the multiple￾choice subsets of each benchmark. 13. More Qualitative Results of VIDEOP2R 13.1. Success Case We provide two additional success cases of VIDEOP2R in [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: Failure Cases of Overly detailed visual configura [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Success Cases of VIDEOP2R <observation>Based on the question, I need to notice the location where the cat stays the longest in the video. Observing the video, the cat is seen initially on a stool, then it jumps down and walks around the room. It briefly interacts with the robot and the orange robot, but most of its time is spent on the stool. The cat does not stay on the carpet, in the cat's nest, or on t… view at source ↗
Figure 20
Figure 20. Figure 20: From base Qwen2.5-VL-7B to VIDEOP2R-SFT and VIDEOP2R: a representative example illustrating the stepwise improvement in model’s perception and reasoning. 8 [PITH_FULL_IMAGE:figures/full_fig_p020_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Examples of Perception Examination: Top Left: Qwen with the text question only; Top Right: Qwen with the text [PITH_FULL_IMAGE:figures/full_fig_p021_21.png] view at source ↗
read the original abstract

Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning. In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning. Our project page is available at https://videop2r.github.io/videop2r/.

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 manuscript introduces VideoP2R, a process-aware reinforcement fine-tuning (RFT) framework for large video language models (LVLMs). It consists of an SFT stage that generates the VideoP2R-CoT-162K process-aware chain-of-thought dataset via a three-step pipeline, and an RL stage that applies a novel process-aware group relative policy optimization (PA-GRPO) algorithm supplying separate rewards for perception and reasoning. The central empirical claim is that this design achieves state-of-the-art performance on six out of seven video reasoning and understanding benchmarks, with ablations confirming the effectiveness of process-aware modeling and that perception outputs are information-sufficient for downstream reasoning.

Significance. If the reported gains hold under rigorous verification, the work would be a meaningful contribution to multimodal video reasoning by explicitly separating perception and reasoning processes in RFT, rather than treating them as a single signal. The release of a 162K-scale process-aware CoT dataset and the PA-GRPO variant could serve as reusable resources for the community. The ablation results on information sufficiency add a useful diagnostic dimension, though the overall significance remains tied to the reproducibility and orthogonality of the claimed improvements.

major comments (2)
  1. [RL stage / PA-GRPO] RL stage / PA-GRPO description: The central claim that separate perception and reasoning rewards in PA-GRPO drive distinct, non-correlated improvements is load-bearing for the process-aware contribution. The manuscript states that PA-GRPO 'supplies separate rewards' but provides no explicit formulation, pseudocode, or computation details (e.g., whether the perception reward uses independent frame-level ground-truth annotations, model-generated consistency checks, or quantities derived from the same video embeddings/CoT traces used for reasoning). If overlap exists, the separation collapses and SotA gains may be attributable to dataset quality or standard GRPO rather than the proposed distinction. This directly tests the orthogonality assumption.
  2. [Experimental results] Experimental results section: The claim of SotA on six out of seven benchmarks is presented without reported error bars, statistical significance tests, or explicit confirmation of benchmark splits and evaluation protocols. Given that the improvements rest on both the new dataset and the modified RL algorithm, the absence of these details makes it difficult to rule out that gains are artifacts of data generation choices or hyperparameter tuning rather than the process-aware design.
minor comments (1)
  1. [Abstract / Method overview] The abstract and method sections use 'process-aware' repeatedly without a concise definition or diagram early in the paper; a single schematic showing the perception/reasoning split would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. The comments help clarify the presentation of the process-aware contributions and strengthen the empirical claims. We address each major comment below and have revised the manuscript accordingly to improve clarity, add missing details, and enhance experimental rigor.

read point-by-point responses

  1. Referee: [RL stage / PA-GRPO] RL stage / PA-GRPO description: The central claim that separate perception and reasoning rewards in PA-GRPO drive distinct, non-correlated improvements is load-bearing for the process-aware contribution. The manuscript states that PA-GRPO 'supplies separate rewards' but provides no explicit formulation, pseudocode, or computation details (e.g., whether the perception reward uses independent frame-level ground-truth annotations, model-generated consistency checks, or quantities derived from the same video embeddings/CoT traces used for reasoning). If overlap exists, the separation collapses and SotA gains may be attributable to dataset quality or standard GRPO rather than the proposed distinction. This directly tests the orthogonality assumption.

    Authors: We agree that the explicit formulation and computation details are essential to substantiate the orthogonality of the process-aware rewards. In the revised manuscript, Section 3.2 now includes the full mathematical definition of PA-GRPO: the joint objective is optimized with separate scalar rewards R_perception (computed from independent frame-level ground-truth annotations generated during the three-step VideoP2R-CoT-162K pipeline) and R_reasoning (derived from final-answer correctness plus step-wise CoT consistency checks that operate on the reasoning trace rather than raw embeddings). We have added Algorithm 1 (pseudocode) in the appendix that shows the group-relative normalization is performed independently per reward type before the combined advantage is used for the policy update. Ablation Table 4 already demonstrates that ablating either reward produces non-overlapping performance drops, supporting that the signals are not redundant. These additions directly address the concern that gains could be explained by dataset quality alone. revision: yes

  2. Referee: [Experimental results] Experimental results section: The claim of SotA on six out of seven benchmarks is presented without reported error bars, statistical significance tests, or explicit confirmation of benchmark splits and evaluation protocols. Given that the improvements rest on both the new dataset and the modified RL algorithm, the absence of these details makes it difficult to rule out that gains are artifacts of data generation choices or hyperparameter tuning rather than the process-aware design.

    Authors: We acknowledge that the original submission lacked sufficient statistical detail. The revised Experimental Results section (Section 4) now reports mean and standard deviation over three independent random seeds for all main-table entries. We have added paired t-tests (p < 0.05) confirming that the reported gains over the strongest baselines are statistically significant on the six benchmarks where VideoP2R is SOTA. Benchmark splits and evaluation protocols are now explicitly stated to match the official test sets and metrics released by each benchmark (e.g., Video-MME, MVBench, etc.). A new paragraph in the appendix discusses hyperparameter sensitivity and confirms that the process-aware gains remain stable across reasonable ranges of the GRPO hyperparameters. These changes allow readers to assess whether the improvements are attributable to the proposed design rather than tuning artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical framework

full rationale

The paper describes an empirical two-stage RFT pipeline: a three-step data generation process yielding VideoP2R-CoT-162K followed by PA-GRPO that supplies separate perception and reasoning rewards. Central claims rest on benchmark accuracy gains and ablation studies rather than any closed mathematical derivation. No equations are presented that define a quantity in terms of itself, no fitted parameter is relabeled as a prediction, and no load-bearing premise reduces to a self-citation or prior ansatz by the same authors. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The framework rests on standard assumptions from LLM reinforcement fine-tuning literature plus the new empirical claim that separate perception/reasoning rewards improve outcomes; no explicit free parameters, axioms, or invented entities are introduced beyond the named algorithm and dataset.

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discussion (0)

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