World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry

• URL: http://arxiv.org/abs/2604.01985v1
• Date: Thu, 02 Apr 2026 12:48:36 GMT
• Title: World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
• Abstract summary: General-purpose world models promise scalable policy evaluation, optimization, and planning.<n>We propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve.
• Score: 82.93104394404781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learning, which primarily focuses on optimal actions, a world model must be reliable over a much broader range of suboptimal actions, which are often insufficiently covered by action-labeled interaction data. To address this challenge, we propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve. The key idea is to decompose action-conditioned state prediction into two factors -- state plausibility and action reachability -- and verify each separately. We show that these verification problems can be substantially easier than predicting future states due to two underlying asymmetries: the broader availability of action-free data and the lower dimensionality of action-relevant features. Leveraging these asymmetries, we augment a world model with (i) a diverse subgoal generator obtained from video corpora and (ii) a sparse inverse model that infers actions from a subset of state features. By enforcing cycle consistency among generated subgoals, inferred actions, and forward rollouts, WAV provides an effective verification mechanism in under-explored regimes, where existing methods typically fail. Across nine tasks spanning MiniGrid, RoboMimic, and ManiSkill, our method achieves 2x higher sample efficiency while improving downstream policy performance by 18%.

Related papers

• Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning [53.77164209208635]
  We introduce WorldEcho, which probes action following over a broader action distribution using visual integrity and trajectory alignment.<n>We propose WorldSync, which strengthens action following along three axes: distributional coverage, representational grounding, and intervention-effect alignment.<n>Experiments on RoboTwin benchmarks and real-robot tasks show that WorldSync improves WorldEcho metrics and serves as a more reliable simulator for iterative policy improvement.
  arXiv  Detail & Related papers  (2026-08-25T17:59:49Z)
• Overcoming Statistical Bias in Action-Controllable World Models [18.890337378174678]
  Action-conditioned world models aim to predict how visual environments evolve under an agent's actions.<n>Future frames are often highly predictable from visual inertia and recurring motion patterns alone.<n>We introduce CoCo, a Counterfactual Consistency framework to enhance action controllability.
  arXiv  Detail & Related papers  (2026-08-05T10:10:36Z)
• Scaling Behavior Foundation Model for Humanoid Robots [77.31115920959364]
  Behavior Foundation Models (BFMs) have emerged as a promising solution to address challenges by leveraging large-scale behavioral data.<n>We show that our approach yields significant improvements in control fidelity and task generalization.<n>These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.
  arXiv  Detail & Related papers  (2026-07-16T16:08:27Z)
• MV-WAM: Manifold-Aware World Action Model with Value Augmentation [45.76770487270348]
  We propose MV-WAM, a novel end-to-end framework that models visual prediction, action generation, and value estimation.<n>We show that MV-WAM achieves a 77.5% mean success rate across four real-world tasks of varying difficulty on a dual-arm robot.
  arXiv  Detail & Related papers  (2026-06-19T04:35:18Z)
• HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models [58.191567345416836]
  World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics.<n>HarmoWAM fully leverages a world model to unify predictive and reactive control, enabling both general transit and precise manipulation.<n>We construct three training-unseen test environments across six real-world robotic tasks, covering variations in background, position, and object semantics.
  arXiv  Detail & Related papers  (2026-05-11T17:59:56Z)
• Beyond State Consistency: Behavior Consistency in Text-Based World Models [58.216587360435305]
  We introduce a new behavior-aligned training paradigm aimed at improving the functional consistency between the world model and the real environment.<n> Experiments on WebShop and TextWorld show that BehR-based training improves long-term alignment in several settings.
  arXiv  Detail & Related papers  (2026-04-15T12:56:45Z)
• Action Draft and Verify: A Self-Verifying Framework for Vision-Language-Action Model [31.013109374489442]
  Vision-Language-Action (VLA) models have recently demonstrated strong performance across embodied tasks.<n>We propose Action-Draft-and-Verify (ADV): diffusion action expert drafts multiple candidate action chunks, and the VLM selects one by scoring all candidates in a single forward pass with a perplexity-style metric.<n>Under matched backbones, training data, and action-chunk length, ADV improves success rate by +4.3 points in simulation and +19.7 points in real-world over diffusion-based baseline.
  arXiv  Detail & Related papers  (2026-03-18T09:16:20Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• World-in-World: World Models in a Closed-Loop World [123.85805788728128]
  We introduce World-in-World, the first open platform that benchmarks world models in a closed-loop world that mirrors real agent-environment interactions.<n>We curate four closed-loop environments that rigorously evaluate diverse WMs, prioritize task success as the primary metric, and move beyond the common focus on visual quality.<n>Our study uncovers three surprises: (1) visual quality alone does not guarantee task success, controllability matters more; (2) scaling post-training with action-observation data is more effective than upgrading the pretrained video generators; and (3) allocating more inference-time compute allows WMs to substantially improve closed
  arXiv  Detail & Related papers  (2025-10-20T22:09:15Z)
• WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning [52.36434784963598]
  We introduce WorldPrediction, a video-based benchmark for evaluating world modeling and procedural planning capabilities of different AI models.<n>We show that current frontier models barely achieve 57% accuracy on WorldPrediction-WM and 38% on WorldPrediction-PP whereas humans are able to solve both tasks perfectly.
  arXiv  Detail & Related papers  (2025-06-04T18:22:40Z)
• GLANCE: Global Actions in a Nutshell for Counterfactual Explainability [10.25011737760687]
  We introduce GLANCE, a versatile and adaptive framework, comprising two algorithms. C-GLANCE employs a clustering approach that considers both the feature space and the space of counterfactual actions. T-GLANCE provides additional features to enhance flexibility.
  arXiv  Detail & Related papers  (2024-05-29T09:24:25Z)
• Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models [0.12499537119440243]
  A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems. We propose foundation world models that embed observations into meaningful and causally latent representations. This enables the surrogate dynamics to directly predict causal future states by leveraging a training-free large language model.
  arXiv  Detail & Related papers  (2024-03-30T20:03:49Z)
• Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity [80.16488817177182]
  GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. We introduce three model stealing attacks to adapt to different actual scenarios.
  arXiv  Detail & Related papers  (2023-12-18T05:42:31Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.