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Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning

Xinyu Liu, Hangjie Yuan, Yujie Wei, Jiazheng Xing, Yujin Han, Jiahao Pan, Yanbiao Ma, Chi-Min Chan, Kang Zhao, Shiwei Zhang, Wenhan Luo, Yike Guo

arXiv:2512.09924v3cs.CV

TL;DR

Reason-informed video editing requires inferring outcomes that literal instructions do not specify, while existing training and evaluation resources and the link between VLM reasoning and generation remain limited. The paper introduces RVE-Bench and proposes ReViSE, which uses an internal VLM to provide differentiable self-reflective training feedback. ReViSE achieves state-of-the-art performance across reasoning categories on RVE-Bench and outperforms the SFT-trained counterpart by 10% in RAVE Overall.

  • Problem

    Reason-informed editing requires inferring underspecified outcomes from physical, temporal, or world knowledge, but remains underexplored and standard reconstruction loss gives no instruction-specific reasoning signal.

    Source: §1 · p. 1

  • Method

    ReViSE uses the model’s internal VLM as a self-reflective evaluator, deriving a differentiable reward from its token-level “Yes” probability to refine generation during training.

    Source: §1 · p. 2

  • Results

    ReViSE achieves state-of-the-art performance across reasoning categories on RVE-Bench and beats the SFT-trained counterpart by 10% in RAVE Overall (4.2422→4.6689).

    Source: §5.3.1 · p. 7, §6 · p. 10

  • Takeaways & Limitations

    The results show that an internal VLM can serve as an intrinsic differentiable reward for reason-informed video editing without external critics or labeled preference data.

    Source: §1 · p. 2, §6 · p. 10

Abstract

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Unified video models exhibit strong capabilities in understanding and generation, yet they struggle with reason-informed visual editing even when equipped with powerful internal vision-language models (VLMs). We attribute this gap to two factors: (1) existing datasets are inadequate for training and evaluating reasoning-aware video editing, and (2) an inherent disconnect between the models' reasoning and editing capabilities, which prevents understanding from guiding the editing process. To address this, we introduce the Reason-Informed Video Editing (RVE) task, which requires reasoning about physical plausibility and causal dynamics during editing. To support systematic evaluation, we construct RVE-Bench, a comprehensive benchmark with two complementary subsets: Reasoning-Aware Video Editing (RAVE) and In-Context Video-to-Video Generation (ICVG), spanning diverse reasoning dimensions across both editing and generation scenarios. Building upon this foundation, we propose ReViSE, a self-reflective learning framework that harnesses the model's internal VLM to evaluate and refine its own generation during training. Unlike prior reward-based approaches that rely on external critics, ReViSE leverages the model's internal VLM as a self-reflective evaluator, providing differentiable feedback that directly refines the generator's reasoning behavior during training. Extensive experiments on RVE-Bench demonstrate that ReViSE enhances editing accuracy and visual fidelity, outperforming the finetuned counterpart by 10% in Overall score on the RAVE subset, demonstrating the effectiveness of self-reflective differentiable reward.

1. Introduction

Reason-informed video editing requires inferring underspecified outcomes from physical, temporal, or world knowledge, a capability that remains underexplored. The paper introduces RVE-Bench and RVE-Dataset, then proposes ReViSE to use a model’s internal VLM for differentiable self-reflection during training.

1. Introduction

  • RVE addresses edits whose intended outcomes are underspecified and must be inferred from physical laws, temporal dynamics, or world knowledge.The paper identifies this reasoning-grounded setting as largely unexplored and notes that current systems can over-edit unintended regions or disrupt scene layout.Source: §1 · p. 1
  • RVE-Bench provides about 1,000 evaluation triplets across RAVE editing and ICVG future-segment generation, while RVE-Dataset supplies 56K reasoning-enriched training triplets.RAVE requires targeted edits inferred through reasoning; ICVG generates plausible new segments from source clips and implicit narrative or causal intent.Source: §1 · pp. 1–2
  • ReViSE uses the model’s internal VLM as an intrinsic differentiable reward, enabling self-reflective training without external reward models or labeled preference data.The framework evaluates edits as a QA task and uses the VLM’s token-level “Yes” probability to provide feedback to the generator.Source: §1 · p. 2

2. Related Work

Prior work advances instruction-guided editing and reasoning-aware visual generation, but the paper identifies a gap in reason-informed video editing and differentiable reward approaches that avoid external critics. ReViSE uses the model’s own frozen VLM as an intrinsic reward.

2.1. Instructional Video Editing

  • Instruction-guided editing spans diffusion-based methods and unified VLM frameworks, including systems that parse complex instructions or couple interpretation with generation [11] [75] [1] [45].The cited approaches address temporal alignment, domain generalization, instruction comprehension, and semantic alignment of edits.Source: §2.1 · pp. 2–3

2.2. Reason-Informed Visual Generation and Editing

  • Reasoning benchmarks and image-editing work assess world knowledge, spatial relations, causal logic, physical plausibility, and contextual consistency [36] [10] [70] [72] [65].The passage also describes reinforcement-learning methods for general alignment, which do not target reason-informed visual editing [12] [17] [19] [25] [28] [33].Source: §2.2 · p. 3
  • Differentiable reward methods backpropagate reward through generators, but ReFL and VADER use pretrained external reward models [40], while RewardDance uses the VLM’s “Yes” probability [57].The paper distinguishes ReViSE by using the model’s own frozen VLM as an intrinsic reward, without an external critic, labeled preference data, or added inference cost [29].Source: §2.2 · pp. 3–4

3. RVE-Dataset and RVE-Bench

RVE-Dataset combines reasoning-enriched editing examples with in-context video generation examples, while RVE-Bench provides a held-out, manually verified evaluation set. The subsets cover complementary reasoning types and use distinct data-construction procedures.

3.1. Data Curation

  • RVE-Dataset pairs source and target videos with reasoning-aware instructions and annotations, covering four reasoning types in each of its RAVE and ICVG subsets.RAVE covers Temporal, Spatial, Physical Causality, and Semantic Inference; ICVG covers Camera, Causal, Emotional, and Commonsense Reasoning.Source: §3.1 · p. 4

3.1.1. Reasoning-Aware Video Editing (RAVE)

  • RAVE rewrites surface-level editing instructions into implicit reasoning cues, using GPT-4o and manually annotated few-shot examples to guide physically motivated and semantically coherent formulations.Examples connect snow-to-moss changes to spring thaw and a color change to a silver facade reflecting the sky.Source: §3.1.1 · p. 4

3.1.2. In-Context V2V Generation (ICVG)

  • ICVG selects visually coherent, contextually overlapping movie clips and uses GPT-4o to synthesize instructions that connect source and target through narrative, causal, or commonsense reasoning.The pipeline groups consecutive scene clips, clusters them using SigLIP embeddings, and selects temporally ordered pairs balancing caption similarity and visual diversity.Source: §3.1.2 · p. 4, Figure 3 · p. 4

3.2. Benchmark Construction

  • RVE-Bench holds out about 1,000 triplets, manually verifies instructions, and evaluates Semantic Consistency and Perceptual Quality through EA, PC, GN, and GR scores.PC is not applicable to ICVG because generating new content leaves no defined boundary between edited and non-edited regions.Source: §3.2 · p. 5

4. Method

ReViSE addresses the disconnect between VLM reasoning and generator editing by adding a self-reflective training loop. It estimates clean videos for VLM evaluation, uses the VLM’s verdict as a differentiable reward, and combines reasoning alignment with generative fidelity.

4.1. Preliminary

  • Connector-based models pass VLM representations into DiT conditioning, but not reasoning constraints; standard SFT optimizes output similarity without reasoning signals.The paper says this can yield visually plausible edits without physical or causal reasoning.Source: §4.1 · p. 5

4.2.2. Self-Reflective Critic.

  • ρ=0.483 (p<10−47) for P(“Yes”) versus ρ=0.271 (p<10−14) for hard-decoded verdicts against human edit accuracy.The soft token probability is used as the reward signal.Source: Figure 4 · p. 5, §4.2.2 · p. 6

4.2. Self-Reflective Learning

  • ReViSE has the generator produce an edit, then backpropagates the frozen VLM’s evaluation through the frozen VAE decoder and clean estimate to generator parameters.This reward path requires neither policy-gradient estimators nor external reward models.Source: §4.2 · p. 5

4.2.1. Clean Video Estimation.

  • A one-step clean estimate makes noisy latents interpretable to U; ReViSE gates critic loss to t ≤ 0.5 and decodes sampled frames to limit VAE overhead.The critic loss is gated because estimates at high noise levels may lack sufficient visual semantics for reliable evaluation.Source: §4.2.1 · p. 6

4.2.2. Self-Reflective Critic.

  • The frozen VLM checks edit accuracy, preservation consistency, naturalness, and realism, returning “Yes” only when all four criteria hold.The paper also instructs the VLM to produce concise reasoning before its verdict.Source: §4.2.2 · p. 6

4.2.3. Training Objective.

  • Unified Semantic Optimization (USO) combines flow matching with a BCE reasoning loss on relative logits, subtracting unchanged-source bias; λ balances fidelity against semantic alignment.The VLM and decoder remain frozen while reasoning-loss gradients update the generator.Source: §4.2.3 · p. 7

5. Experiments

Experiments evaluate ReViSE on reasoning-informed and conventional editing tasks, finding gains over baselines and SFT while probing training objectives and reward-signal settings.

5.1. Implementation Details

  • Self-reflection at low-noise timesteps limits amortized training latency to 5.09 s/it while preserving 3.513 P/step inference FLOPs.ReViSE adds zero deployment overhead.Source: §5.1 · p. 7

5.2. Baselines and Evaluation

  • RVE-Bench has ∼1K test samples across RAVE and ICVG, assessed with VLM editing, preservation, naturalness, realism, and Overall scores plus video-quality metrics.PC is omitted for ICVG, where strict source preservation is not meaningful.Source: §5.2 · p. 7

5.3.1. Quantitative Results.

  • On RAVE, ReViSE scores Overall 4.6689, 49% above Omni-Video (3.1331→4.6689) and 10% above SFT (4.2422→4.6689); ICVG Overall rises 3.8395→3.9576.RAVE EA rises from 4.8506 to 6.1605 (+27%), while PC rises from 2.9716 to 5.4086 (+82%).Source: §5.3.1 · p. 7, Table 1 · p. 8
  • On VIE-Bench, ReViSE leads in Overall (6.3819), EA (6.7312), and GN (7.3656) under GPT-4o-rewritten reasoning instructions.Source: Table 2 · p. 8, §5.3.1 · p. 8
  • On Ditto-1M, ReViSE beats all baselines in ViCLIPT, EA, GN, GR, and Overall, gaining 36.7% in Overall (2.7871→3.8109) over the previous best.Source: Table 3 · p. 8, §5.3.1 · p. 8
  • Qualitative examples show contextually plausible edits and logs-to-chips generation, while 15-expert comparisons give ReViSE the highest win rate on all three criteria.The criteria are Edit Accuracy, Content Preservation, and Visual Quality.Source: §5.3.1 · p. 8, Figure 6 · p. 9, Figure 7 · p. 9

5.4. Ablation Study

  • On RAVE, USO achieves the best Overall score by jointly optimizing flow-matching and reasoning losses with independent gradients.SFT focuses on reconstruction fidelity but sacrifices EA and PC, while RWR scales flow-matching loss by a reward-derived weight.Source: §5.4 · pp. 9–10, Table 4 · p. 10
  • λ=0.25 gives best Overall; σmax=0.5 retains 93.4% of clean-video upper-bound correlation (r=0.483) at a 25.0% trigger rate.σmax=0.1 yields the highest correlation but only 3.5% trigger coverage; reward quality deteriorates sharply above 0.5.Source: Table 5 · p. 10, Table 6 · p. 10, §5.4 · p. 10

6. Conclusion

The conclusion presents RVE as a gap between literal instruction following and reasoning-grounded editing, and ReViSE as a way to connect a model’s reasoning and generation. It reports state-of-the-art RVE-Bench performance and generalization to conventional editing benchmarks.

  • RVE targets the underexplored gap between literal instruction following and reasoning-grounded video editing.Source: §6 · p. 10
  • RVE-Dataset and RVE-Bench provide large-scale training data and the first dedicated benchmark spanning diverse reasoning dimensions.Source: §6 · p. 10
  • ReViSE uses the model’s frozen internal VLM as an intrinsic differentiable reward, without external critics or labeled preference data.Source: §6 · p. 10
  • Experiments report state-of-the-art performance across RVE-Bench reasoning categories and generalization to conventional video-editing benchmarks.Source: §6 · p. 10
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