EVA: Aligning Video World Models with Executable Robot Actions via Inverse Dynamics Rewards

arXiv 26.3 2026 43.5 method, application

TLDR

EVA uses inverse dynamics rewards to align video world models with executable robot actions, addressing the executability gap via RL post-training.

Reasoning

The paper clearly identifies a practical problem (executability gap) and proposes a novel RL-based alignment method using inverse dynamics as reward. Strengths include direct use of real robot trajectories and robustness to visual artifacts. Weaknesses include potential embodiment specificity and reliance on pre-collected real data.

Read-first score

Read-first score 43.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 55.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Topical relevance 25%

78.6

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark,experiment,result

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=artifact,code,github

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 607.

Keyword Scores

world model

video world model

generative world model

world dynamics prediction

world simulator

model-based reinforcement learning world model

interactive world model

Deep Analysis

Innovations

Leveraging the executability gap as a training signal for video world models
Repurposing an inverse dynamics model (IDM) trained on real robot trajectories as a reward model for video alignment
Reinforcement-learning post-training framework (EVA) to align video world models with executable robot actions

Methodology

EVA is a reinforcement-learning post-training framework that trains an inverse dynamics model (IDM) on real robot trajectories and uses it as a reward model to evaluate generated videos through the action sequences they induce. The reward encourages smooth motions (measured by velocity, acceleration, and jerk) and penalizes actions that violate embodiment constraints, even when generated videos contain severe visual artifacts.

Key Results

Experiments on the RoboTwin benchmark and a real bimanual robot show that EVA reduces embodiment-specific artifacts in generated rollouts and improves downstream task execution success.

Resources

Paper Project