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arxiv: 2604.01824 · v2 · submitted 2026-04-02 · 💻 cs.CV

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STRIVE: Structured Spatiotemporal Exploration for Reinforcement Learning in Video Question Answering

Emad Bahrami, Juergen Gall, Mohsen Fayyaz, Olga Zatsarynna, Parth Pathak, Sunando Sengupta

Authors on Pith no claims yet

Pith reviewed 2026-05-13 21:08 UTC · model grok-4.3

classification 💻 cs.CV
keywords STRIVEreinforcement learningvideo question answeringspatiotemporal variantsmultimodal modelspolicy optimizationimportance-aware sampling
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The pith

By generating multiple spatiotemporal variants of videos and jointly normalizing rewards across text and visuals, STRIVE stabilizes reinforcement learning for video question answering.

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

The paper introduces STRIVE, a reinforcement learning framework for video question answering that addresses low reward variance in group-based policy optimization. It creates multiple structured spatiotemporal variants of each input video and performs joint normalization over both textual generations and these visual variants to produce richer advantage estimates. An importance-aware sampling step prioritizes question-relevant frames while preserving temporal coverage. This design yields more stable policy updates and avoids overfitting to any single video configuration. Experiments across six benchmarks show consistent gains over strong RL baselines in large multimodal models.

Core claim

STRIVE constructs multiple spatiotemporal variants of each input video and performs joint normalization across both textual generations and visual variants to enrich reward signals and promote more stable and informative policy updates. It adds an importance-aware sampling mechanism that prioritizes frames most relevant to the question, encouraging robust reasoning across complementary visual perspectives rather than overfitting to one spatiotemporal configuration.

What carries the argument

Joint normalization across textual generations and structured spatiotemporal visual variants, paired with importance-aware frame sampling to keep exploration semantically grounded.

If this is right

  • More stable advantage estimates when textual responses share similar correctness levels.
  • Improved robustness through reasoning across multiple complementary visual perspectives.
  • Consistent performance gains on VideoMME, TempCompass, VideoMMMU, MMVU, VSI-Bench, and PerceptionTest.
  • Lower risk of overfitting to any single spatiotemporal video configuration.

Where Pith is reading between the lines

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

  • The same variant-generation and joint-normalization pattern could transfer to other multimodal RL settings such as video captioning or temporal action localization.
  • Importance-aware sampling might be combined with uncertainty-driven exploration to further reduce the number of variants needed.
  • Analogous structured perturbations could help address reward sparsity in static-image question-answering models that currently rely only on linguistic diversity.

Load-bearing premise

Joint normalization across textual generations and structured visual variants will enrich reward signals and produce stable policy updates without introducing new biases or causing the model to overfit to the chosen perturbations.

What would settle it

Running the same large multimodal models on the six listed benchmarks with STRIVE disabled would show no gain or a drop relative to the RL baselines.

Figures

Figures reproduced from arXiv: 2604.01824 by Emad Bahrami, Juergen Gall, Mohsen Fayyaz, Olga Zatsarynna, Parth Pathak, Sunando Sengupta.

Figure 1
Figure 1. Figure 1: STRIVE prevents advantage collapse in video reinforcement learning. (a) Standard policy optimization samples multiple textual responses from a single, fixed video representation. When these generated responses lack diversity, the reward variance can drop to zero (Var(r) = 0), stalling the gradient of the objective J(θ). (b) Our proposed STRIVE framework employs Variant Construction to systematically genera… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our STRIVE Framework. Given a video input v and question q, a set of transformations {Ti}M i=1 is applied to generate video variants {v˜i}M i=1. Each variant is paired with the question to form input tuples (˜vi , q). The policy model produces G textual responses for each of the M variants, resulting in a G × M matrix of responses {oi,j} G,M i=1,j=1. These are evaluated by a reward model to obt… view at source ↗
Figure 3
Figure 3. Figure 3: Empirical analysis of optimization dynamics. (a) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison between STRIVE and the standard GRPO baseline. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for video question answering. While group-based policy optimization methods have shown promise in large multimodal models, they often suffer from low reward variance when responses exhibit similar correctness, leading to weak or unstable advantage estimates. STRIVE addresses this limitation by constructing multiple spatiotemporal variants of each input video and performing joint normalization across both textual generations and visual variants. By expanding group comparisons beyond linguistic diversity to structured visual perturbations, STRIVE enriches reward signals and promotes more stable and informative policy updates. To ensure exploration remains semantically grounded, we introduce an importance-aware sampling mechanism that prioritizes frames most relevant to the input question while preserving temporal coverage. This design encourages robust reasoning across complementary visual perspectives rather than overfitting to a single spatiotemporal configuration. Experiments on six challenging video reasoning benchmarks including VideoMME, TempCompass, VideoMMMU, MMVU, VSI-Bench, and PerceptionTest demonstrate consistent improvements over strong reinforcement learning baselines across multiple large multimodal models. Our results highlight the role of structured spatiotemporal exploration as a principled mechanism for stabilizing multimodal reinforcement learning and improving video reasoning performance.

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 introduces STRIVE, a reinforcement learning framework for video question answering that constructs multiple spatiotemporal variants of each input video via importance-aware frame sampling and performs joint normalization across both textual generations and visual variants. This is intended to increase reward variance and produce more stable advantage estimates than standard group-based policy optimization. The manuscript claims consistent improvements over strong RL baselines across six video reasoning benchmarks (VideoMME, TempCompass, VideoMMMU, MMVU, VSI-Bench, PerceptionTest) on multiple large multimodal models.

Significance. If the empirical results and the exchangeability assumption hold, STRIVE would provide a concrete mechanism for enriching reward signals in multimodal RL without external data, addressing a known limitation of low-variance advantages in group-based methods for video reasoning tasks.

major comments (2)
  1. [Abstract and Experiments] Abstract and Experiments section: the central claim of 'consistent improvements' over baselines is unsupported by any quantitative results, tables, baseline specifications, ablation studies, or statistical tests in the provided manuscript text. This is load-bearing for the contribution and prevents verification of effect size or reliability.
  2. [Method] Method section on joint normalization: the procedure assumes that rewards from importance-aware spatiotemporal variants are exchangeable with the original video distribution so that normalization yields unbiased advantages. No analysis or ablation is shown to confirm that frame sampling does not systematically alter question difficulty or reward distributions, which would make normalized advantages reflect perturbation artifacts rather than response quality.
minor comments (1)
  1. [Method] Clarify the precise mathematical definition of the joint normalization operator and how advantages are aggregated across the combined textual-visual group.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback. We address each major comment point by point below, outlining planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: the central claim of 'consistent improvements' over baselines is unsupported by any quantitative results, tables, baseline specifications, ablation studies, or statistical tests in the provided manuscript text. This is load-bearing for the contribution and prevents verification of effect size or reliability.

    Authors: We acknowledge that the excerpt provided for review did not include the full experimental results. The complete manuscript contains Section 4 with Table 1 reporting performance metrics across the six benchmarks (VideoMME, TempCompass, VideoMMMU, MMVU, VSI-Bench, PerceptionTest), baseline specifications (including standard group-based RL methods), ablation studies on the sampling and normalization components, and available statistical comparisons. We will revise the manuscript to ensure these quantitative results, tables, and analyses are explicitly referenced from the abstract and prominently featured in the experiments section to allow full verification of effect sizes and reliability. revision: yes

  2. Referee: [Method] Method section on joint normalization: the procedure assumes that rewards from importance-aware spatiotemporal variants are exchangeable with the original video distribution so that normalization yields unbiased advantages. No analysis or ablation is shown to confirm that frame sampling does not systematically alter question difficulty or reward distributions, which would make normalized advantages reflect perturbation artifacts rather than response quality.

    Authors: We agree this is an important point about the exchangeability assumption underlying the joint normalization. In the revised manuscript we will add a dedicated analysis subsection (likely in Experiments or an appendix) comparing reward distributions and question difficulty metrics between original videos and the generated spatiotemporal variants, including statistical tests for systematic shifts. We will also include an ablation study isolating the importance-aware sampling to quantify its effect. While our design prioritizes frames relevant to the question to maintain semantic equivalence and temporal coverage, we recognize that empirical validation is required and will provide it to rule out perturbation artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method and claims are empirically grounded

full rationale

The paper introduces STRIVE as a structured RL framework that augments group-based policy optimization with spatiotemporal video variants and joint normalization across textual generations and visual perturbations. No equations, derivations, or self-referential definitions appear in the manuscript that would reduce the claimed advantages or policy updates to fitted parameters, self-citations, or inputs by construction. The core mechanism is described procedurally, with importance-aware sampling presented as a design choice rather than a derived necessity. All performance claims rest on experiments across six independent external benchmarks (VideoMME, TempCompass, etc.), which serve as falsifiable evidence outside the method's own definitions. No load-bearing uniqueness theorems or ansatzes are imported via self-citation chains. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no free parameters, axioms, or invented entities are explicitly identified or required for the central claim.

pith-pipeline@v0.9.0 · 5525 in / 1090 out tokens · 41693 ms · 2026-05-13T21:08:44.446346+00:00 · methodology

discussion (0)

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