Introduces textual belief states and factorized GRPO to enforce strict latent state mediation in text-based world models, yielding preserved prediction accuracy with large gains in representation quality and rollout performance on TextWorld and ScienceWorld.
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Dream to Control: Learning Behaviors by Latent Imagination
Canonical reference. 95% of citing Pith papers cite this work as background.
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abstract
Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
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- abstract Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual contro
- background Langugae-Conditoned MoCoGAN [29], U-Net [30], Latte [ 31], Wan [32], Sora 2 [ 33]. . . Embodied World Model SWIM [34], DreamDojo [ 35], RoboDreamer [36], RoboScape [37]. . . WM for VLA Imitation Learning Ctrl-World [38], RoboScape [37], DREMA [ 39] Reinforcement Learning Dreamer to Control [ 40] DreamerV2 [ 41], Dreamer 4 [ 42], RISE [ 43] DreamerV3 [44], DayDreamer [45], World-Env [46], RoboScape-R [47] WMPO [48], WoVR [49], VLA-RFT [50], RWML [51], MoDem-V2 [52] World-Gymnast [53], RWM-U [54],
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representative citing papers
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citing papers explorer
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Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Dreamer-CDP achieves reconstruction-free world modeling via a JEPA-style predictor on continuous deterministic representations and matches Dreamer's performance on Crafter.
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World Action Models are Zero-shot Policies
DreamZero uses a 14B video diffusion model as a World Action Model to achieve over 2x better zero-shot generalization on real robots than state-of-the-art VLAs, real-time 7Hz closed-loop control, and cross-embodiment transfer with 10-30 minutes of data.
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RISE: Self-Improving Robot Policy with Compositional World Model
RISE combines a controllable dynamics model and progress value model into a closed-loop self-improving pipeline that updates robot policies entirely in imagination, reporting over 35% absolute gains on three real-world tasks.
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DynaWeb: Model-Based Reinforcement Learning of Web Agents
DynaWeb introduces a model-based RL framework that trains web agents via imagined rollouts in a learned web world model interleaved with real expert trajectories, yielding consistent gains on WebArena and WebVoyager benchmarks.
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Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
Single-stage fine-tuning of a video model to generate actions as latent frames plus future states and values yields state-of-the-art robot policy performance on LIBERO, RoboCasa, and bimanual tasks.
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Ctrl-World: A Controllable Generative World Model for Robot Manipulation
A controllable world model trained on the DROID dataset generates consistent multi-view robot trajectories for over 20 seconds and improves generalist policy success rates by 44.7% via imagined trajectory fine-tuning.
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DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
DAWM introduces a modular diffusion world model with an inverse dynamics model to produce complete synthetic transitions that improve conservative offline RL algorithms like TD3BC and IQL on D4RL tasks.
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HERO: Hierarchical Extrapolation and Refresh for Efficient World Models
HERO accelerates world model inference 1.73x via hierarchical patch-wise refresh in shallow layers and linear extrapolation in deeper layers with minimal quality loss.
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GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation
GAF creates 4D dynamic scene models by adding motion to 3D Gaussians, enabling better reconstruction and 7.3% higher success in robotic tasks.
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V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
V-JEPA 2 pre-trained on massive unlabeled video achieves strong results on motion understanding and action anticipation, SOTA video QA at 8B scale, and enables zero-shot robotic planning on Franka arms using only 62 hours of unlabeled robot video.
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DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
DINO-WM builds world models on pre-trained DINOv2 features to enable zero-shot planning from offline data without rewards or demonstrations.
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Reasoning with Language Model is Planning with World Model
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R3M: A Universal Visual Representation for Robot Manipulation
A visual encoder pre-trained on diverse human videos with contrastive and language objectives improves simulated robot manipulation success by over 20% versus training from scratch and enables real Franka arm tasks from 20 demonstrations.
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The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
The FIL Hypothesis claims that inductive biases outperform purely data-driven methods on GPU programming tasks with non-trivial feedback loops.
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Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data
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Perceptual 3D Simulation With Physical World Modeling
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$\tau_0$-WM: A Unified Video-Action World Model for Robotic Manipulation
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Physically Viable World Models: A Case for Query-Conditioned Embodied AI
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Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics
TRM trains a small horizon-matched pairwise head on trajectory data to improve terminal-state ranking in latent MPC, raising success from 7% to 97% on TwoRoom and 32.7% to 84% on PLDM without changing the encoder or dynamics.
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stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
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Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
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Neural Computers
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UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
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EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks
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Can Predicted Dynamics Exist in the Physical World?
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World Action Models: The Next Frontier in Embodied AI
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World Simulation with Video Foundation Models for Physical AI
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World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
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Cosmos World Foundation Model Platform for Physical AI
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Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends
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Transfer Learning for Customized Car Racing Environments
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