Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization

• URL: http://arxiv.org/abs/2508.20294v1
• Date: Wed, 27 Aug 2025 22:02:56 GMT
• Title: Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization
• Authors: Frank Röder, Jan Benad, Manfred Eppe, Pradeep Kr. Banerjee,
• Abstract summary: We introduce Dynamics-Aligned Latent Imagination (DALI), a framework that infers latent context representations from agent-environment interactions.<n>DALI generates actionable representations conditioning the world model and policy, bridging perception and control.<n>On challenging cMDP benchmarks, DALI achieves significant gains over context-unaware baselines.
• Score: 1.6332728502735252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world reinforcement learning demands adaptation to unseen environmental conditions without costly retraining. Contextual Markov Decision Processes (cMDP) model this challenge, but existing methods often require explicit context variables (e.g., friction, gravity), limiting their use when contexts are latent or hard to measure. We introduce Dynamics-Aligned Latent Imagination (DALI), a framework integrated within the Dreamer architecture that infers latent context representations from agent-environment interactions. By training a self-supervised encoder to predict forward dynamics, DALI generates actionable representations conditioning the world model and policy, bridging perception and control. We theoretically prove this encoder is essential for efficient context inference and robust generalization. DALI's latent space enables counterfactual consistency: Perturbing a gravity-encoding dimension alters imagined rollouts in physically plausible ways. On challenging cMDP benchmarks, DALI achieves significant gains over context-unaware baselines, often surpassing context-aware baselines in extrapolation tasks, enabling zero-shot generalization to unseen contextual variations.

Related papers

• Aligning Agentic World Models via Knowledgeable Experience Learning [68.85843641222186]
  We introduce WorldMind, a framework that constructs a symbolic World Knowledge Repository by synthesizing environmental feedback.<n>WorldMind achieves superior performance compared to baselines with remarkable cross-model and cross-environment transferability.
  arXiv  Detail & Related papers  (2026-01-19T17:33:31Z)
• From Word to World: Can Large Language Models be Implicit Text-based World Models? [82.47317196099907]
  Agentic reinforcement learning increasingly relies on experience-driven scaling.<n>World models offer a potential way to improve learning efficiency through simulated experience.<n>We study whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents.
  arXiv  Detail & Related papers  (2025-12-21T17:28:42Z)
• Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs [49.66344956133349]
  Reasoning capacity shapes both inference-time performance and reinforcement learning (RL) training for large (vision-) language models.<n>This paper proposes Reasoning Palette, a novel latent-modulation framework that endows the model with a latent variable for strategic contextualization.
  arXiv  Detail & Related papers  (2025-12-19T03:32:53Z)
• Dynamic Context-Aware Scene Reasoning Using Vision-Language Alignment in Zero-Shot Real-World Scenarios [0.0]
  This work introduces a Dynamic Context-Aware Scene Reasoning framework to address zero-shot real-world scenarios.<n>The proposed approach integrates pre-trained vision transformers and large language models to align visual semantics with natural language descriptions.<n>Experiments demonstrate up to 18% improvement in scene understanding accuracy over baseline models in complex and unseen environments.
  arXiv  Detail & Related papers  (2025-10-30T15:07:55Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• Generalizing Large Language Model Usability Across Resource-Constrained [0.43512163406552007]
  dissertation presents a systematic study toward generalizing Large Language Models under real-world constraints.<n>First, it introduces a robust text-centric alignment framework that enables LLMs to seamlessly integrate diverse modalities.<n>Beyond multimodal setting, the dissertation investigates inference-time optimization strategies for LLMs.
  arXiv  Detail & Related papers  (2025-05-13T01:00:12Z)
• EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks [24.41705039390567]
  EmbodiedVSR (Embodied Visual Spatial Reasoning) is a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning.<n>Our method enables zero-shot spatial reasoning without task-specific fine-tuning.<n>Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence.
  arXiv  Detail & Related papers  (2025-03-14T05:06:07Z)
• A Survey on Post-training of Large Language Models [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.<n>These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance.<n>This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures ethical coherence and alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Integration and Adaptation, which
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity [0.0]
  Large Language Models (LLMs) are increasingly sophisticated and ubiquitous in natural language processing (NLP) applications. This paper presents a novel framework for contextual grounding in textual models, with a particular emphasis on the Context Representation stage. Our findings have significant implications for the deployment of LLMs in sensitive domains such as healthcare, legal systems, and social services.
  arXiv  Detail & Related papers  (2024-08-07T18:12:02Z)
• Learning Latent Dynamic Robust Representations for World Models [9.806852421730165]
  Visual Model-Based Reinforcement Learning (MBL) promises to agent's knowledge about the underlying dynamics of the environment. Top-temporal agents such as Dreamer often struggle with visual pixel-based inputs in the presence of irrelevant noise in the observation space. We apply a-temporal masking strategy, combined with latent reconstruction, to capture endogenous task-specific aspects of the environment for world models.
  arXiv  Detail & Related papers  (2024-05-10T06:28:42Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)

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.