{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3","slug":"mastering-diverse-domains-through-world-models-dreamerv3","name":"Mastering Diverse Domains through World Models (DreamerV3)","type":"product","url":"https://arxiv.org/abs/2301.04104","page_url":"https://unfragile.ai/mastering-diverse-domains-through-world-models-dreamerv3","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_0","uri":"capability://planning.reasoning.world.model.based.reinforcement.learning.with.latent.imagination","name":"world-model-based reinforcement learning with latent imagination","description":"DreamerV3 learns a compact world model that predicts future states in a learned latent space, then uses this model to plan and train policies through imagination without requiring environment interaction for every gradient step. The architecture uses a variational autoencoder (VAE) to compress observations into a latent representation, a recurrent state-space model to predict latent dynamics, and a decoder to reconstruct observations. Policy and value functions are trained on imagined trajectories generated by rolling out the world model, dramatically reducing sample complexity compared to model-free RL.","intents":["Train RL agents with 10-100x fewer environment interactions by planning in learned latent space","Enable agents to learn from visual observations without hand-crafted state representations","Generalize learned behaviors across diverse tasks and visual domains without task-specific tuning","Reduce computational cost of RL training by amortizing world model predictions across multiple policy rollouts"],"best_for":["Researchers training embodied AI agents on visual control tasks with limited environment interaction budgets","Teams building robotics systems where real-world interaction is expensive or dangerous","Organizations scaling RL to diverse visual domains (games, simulations, real-world video) without per-domain engineering"],"limitations":["World model quality bottlenecks policy performance — errors compound over long imagined rollouts (>50 steps), limiting planning horizon","Requires sufficient diversity in training data to learn generalizable latent representations; fails on out-of-distribution visual inputs","Computational overhead of VAE encoding/decoding and recurrent state prediction adds ~2-5x wall-clock time vs model-free baselines during training","Latent space interpretability is limited; debugging policy failures requires analyzing high-dimensional learned representations","No built-in mechanism for uncertainty quantification in world model predictions, limiting safe exploration in real-world deployment"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM for training on image observations","Environment with visual observations (pixels) or ability to extract visual features","Minimum 10k environment steps for meaningful world model learning"],"input_types":["visual observations (RGB images, 64x64 to 256x256 resolution)","action sequences (discrete or continuous)","reward signals (scalar or vector)","terminal/done flags"],"output_types":["learned latent state representations (typically 32-256 dimensional vectors)","predicted next latent states","reconstructed observations from latent space","policy action distributions","value function estimates","imagined trajectory rollouts"],"categories":["planning-reasoning","reinforcement-learning","world-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_1","uri":"capability://planning.reasoning.multi.task.visual.policy.learning.with.task.agnostic.world.models","name":"multi-task visual policy learning with task-agnostic world models","description":"DreamerV3 learns a single world model that captures visual dynamics common across multiple tasks, then trains separate task-specific policy heads that leverage the shared latent representation. The world model is trained on a mixture of trajectories from different tasks without explicit task conditioning, discovering task-invariant visual features (object motion, physics) that transfer across diverse objectives. Task-specific policies are trained through imagination using the shared world model, enabling rapid adaptation to new tasks with minimal additional data.","intents":["Train a single visual model on diverse robotic manipulation tasks and reuse it for new tasks with minimal retraining","Learn generalizable visual representations that capture object dynamics without task-specific supervision","Reduce data collection burden by sharing world model across multiple related control problems","Enable zero-shot or few-shot transfer to visually similar tasks by leveraging pre-trained world model"],"best_for":["Robotics teams managing multiple manipulation or navigation tasks with shared visual environment","Researchers studying transfer learning and generalization in embodied AI","Organizations building multi-task agents where environment interaction is the bottleneck"],"limitations":["Task-agnostic world model may not capture task-specific visual features (e.g., subtle object properties relevant only to one task)","Performance degrades when tasks have conflicting visual dynamics or require fundamentally different state representations","Requires careful data balancing across tasks during training; imbalanced task mixtures lead to poor world model quality on underrepresented tasks","No explicit mechanism for task identification or context switching; policies must learn task-specific behavior from shared latent space alone"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 12GB+ VRAM for multi-task training","Minimum 50k environment steps across all tasks for meaningful transfer","Consistent visual observation format across all tasks"],"input_types":["visual observations from multiple tasks (RGB images)","action sequences (task-specific action spaces supported)","per-task reward signals","task identifiers or trajectory labels (optional)"],"output_types":["shared latent world model","task-specific policy parameters","task-specific value function estimates","imagined multi-task rollouts"],"categories":["planning-reasoning","automation-workflow","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_10","uri":"capability://planning.reasoning.grounding.large.language.models.in.interactive.environments.with.online.rl.glam","name":"grounding large language models in interactive environments with online rl (glam)","description":"DreamerV3 is extended in the GLAM framework to ground large language models (LLMs) in interactive environments through online RL. The approach uses an LLM to generate high-level task descriptions or reward functions, which are then used to train RL agents in simulated or real environments. The agent learns a world model of the environment and uses it to optimize policies that maximize the LLM-specified rewards. This enables LLMs to interact with and learn from environments without explicit programming of reward functions or environment dynamics.","intents":["Enable LLMs to specify and optimize for complex, natural-language-defined objectives in interactive environments","Ground LLM knowledge in environment-specific dynamics through online RL","Enable LLMs to learn from environment feedback and adapt their objectives based on interaction results"],"best_for":["Researchers studying the integration of LLMs with embodied AI and RL","Teams building agents that can be controlled through natural language instructions","Organizations exploring how LLMs can guide RL agent learning in complex environments"],"limitations":["LLM-generated reward functions may be misaligned with intended objectives; requires careful prompt engineering and validation","Computational cost is high due to LLM inference + RL training; ~10-100x wall-clock time vs. hand-crafted rewards","LLM knowledge may not transfer well to environment-specific dynamics; agents may struggle with tasks that require learning environment-specific patterns","No explicit mechanism for handling LLM errors or hallucinations; agents may optimize for spurious reward signals","Requires careful design of the interface between LLM and RL agent to ensure reward signals are well-defined and learnable"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 12GB+ VRAM","Access to an LLM (e.g., GPT-3, GPT-4, or open-source alternatives)","Interactive environment (simulation or real-world)"],"input_types":["natural language task descriptions from LLM","environment observations","environment interactions"],"output_types":["LLM-generated reward functions","learned policies optimized for LLM-specified objectives","environment interaction trajectories"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_2","uri":"capability://planning.reasoning.continuous.and.discrete.action.space.handling.with.unified.latent.planning","name":"continuous and discrete action space handling with unified latent planning","description":"DreamerV3 handles both continuous (robotic control) and discrete (Atari games) action spaces through a unified policy parameterization in the learned latent space. The policy network outputs action distributions (Gaussian for continuous, categorical for discrete) that are sampled during imagination rollouts. The world model's dynamics function is action-agnostic, treating actions as inputs to the recurrent state predictor without architectural changes, enabling seamless switching between control modalities.","intents":["Train a single codebase on both continuous robotic control and discrete game-playing tasks without branching logic","Generalize world model learning across heterogeneous action spaces in multi-domain training","Simplify deployment by using identical inference code for different action space types"],"best_for":["Researchers benchmarking RL algorithms across diverse domains (continuous + discrete)","Teams building general-purpose embodied AI systems that interact with multiple environment types","Organizations seeking unified RL infrastructure to reduce engineering complexity"],"limitations":["No explicit action space constraints in the latent policy; requires post-hoc clipping or rejection sampling to enforce action bounds","Discrete action spaces with >100 actions become computationally expensive due to categorical distribution sampling","Mixed continuous-discrete action spaces (e.g., gripper position + open/close) require custom policy head design","Action exploration strategy (entropy regularization) must be tuned per action space type for optimal performance"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM","Environment with well-defined action space (continuous or discrete)"],"input_types":["visual observations","continuous actions (float vectors, arbitrary dimensionality)","discrete actions (integer indices, up to ~100 actions)","reward signals"],"output_types":["action distributions (Gaussian or categorical)","sampled actions for imagination rollouts","policy gradients"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_3","uri":"capability://planning.reasoning.imagination.based.policy.optimization.with.latent.rollouts","name":"imagination-based policy optimization with latent rollouts","description":"DreamerV3 trains policies by rolling out imagined trajectories in the learned latent space, computing policy gradients without environment interaction. The process involves: (1) sampling initial latent states from the world model's prior, (2) rolling out the policy in imagination for H steps, (3) computing returns using the value function, and (4) backpropagating policy gradients through the imagined trajectory. The world model is frozen during policy optimization, enabling efficient amortization of world model computation across multiple policy updates.","intents":["Optimize policies with 10-100x fewer environment interactions by training on imagined rollouts","Decouple world model learning from policy learning to enable independent optimization schedules","Reduce variance in policy gradient estimates by leveraging learned value functions on imagined trajectories"],"best_for":["Sample-constrained RL applications (robotics, real-world systems) where environment interaction is expensive","Researchers studying the interplay between world model quality and policy performance","Teams optimizing for wall-clock training time rather than environment steps"],"limitations":["Policy performance is upper-bounded by world model quality; compounding errors in long imagined rollouts (>50 steps) degrade policy learning","Imagination rollout length (H) is a critical hyperparameter; too short limits planning horizon, too long amplifies world model errors","Value function bootstrapping on imagined states introduces bias if the value function is poorly calibrated","No mechanism to detect and correct for world model distribution shift; policies may exploit world model errors rather than learning robust behaviors","Requires careful tuning of imagination rollout length and world model update frequency to balance stability and sample efficiency"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM","Pre-trained or jointly-trained world model","Value function network"],"input_types":["initial latent states (sampled from world model prior)","policy network parameters","value function network parameters","imagination rollout length (H)"],"output_types":["policy gradients","value function targets","imagined trajectory returns"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_4","uri":"capability://data.processing.analysis.symlog.reward.scaling.for.multi.scale.reward.normalization","name":"symlog reward scaling for multi-scale reward normalization","description":"DreamerV3 introduces symlog (symmetric logarithm) scaling to handle rewards spanning 10+ orders of magnitude without task-specific normalization. The symlog function applies log scaling to large-magnitude rewards while preserving linear scaling for small rewards, enabling a single value function and reward prediction head to handle both sparse rewards (e.g., game scores of 0-1000) and dense rewards (e.g., continuous control with rewards in [-1, 1]). This is applied to both reward prediction in the world model and value function targets, eliminating the need for per-task reward normalization.","intents":["Train a single agent on diverse tasks with wildly different reward scales (Atari scores vs. robotic control rewards)","Eliminate manual reward normalization and task-specific hyperparameter tuning","Improve value function stability when rewards span multiple orders of magnitude"],"best_for":["Multi-task RL systems combining tasks with heterogeneous reward structures","Researchers building general-purpose RL agents across diverse domains","Teams seeking to reduce hyperparameter tuning burden in RL training"],"limitations":["Symlog scaling introduces a learnable parameter (scale) that must be tuned; poor scale choices degrade performance","Inverse symlog transformation adds computational overhead (~1-2% per forward pass) compared to linear scaling","Symlog scaling assumes reward distributions are roughly log-normal; may not be optimal for bimodal or heavy-tailed reward distributions","Gradient flow through symlog transformation can be unstable for very large rewards; requires careful initialization"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","Rewards as scalar or vector values"],"input_types":["raw reward signals (any magnitude)","reward scale hyperparameter"],"output_types":["symlog-scaled rewards","inverse-symlog-transformed value estimates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_5","uri":"capability://planning.reasoning.joint.world.model.and.policy.training.with.shared.latent.representation","name":"joint world model and policy training with shared latent representation","description":"DreamerV3 trains the world model and policy jointly using a unified loss function that combines reconstruction, dynamics, and policy objectives. The world model learns to compress observations into a latent space that is simultaneously useful for predicting future states and for learning control policies. The policy and value function are trained on imagined rollouts from the world model, creating a feedback loop where policy performance informs which latent features are most useful for control. This joint training is enabled by a shared encoder/decoder architecture and careful balancing of loss weights.","intents":["Learn latent representations that are optimized for both world modeling and control, avoiding task-irrelevant visual features","Improve sample efficiency by having the policy guide world model learning toward control-relevant features","Simplify architecture design by using a single encoder/decoder for both world modeling and policy learning"],"best_for":["Sample-constrained RL applications where every bit of data must be leveraged for learning","Researchers studying the relationship between world model quality and policy performance","Teams building end-to-end RL systems with limited computational budgets"],"limitations":["Joint training can lead to instability if loss weights are poorly balanced; world model may overfit to policy-relevant features at the expense of general dynamics modeling","Computational cost is higher than separate training due to shared gradient computation; ~1.5-2x wall-clock time vs. sequential training","Debugging failures is harder because world model and policy are tightly coupled; poor policy performance may be due to either component","Requires careful initialization and learning rate scheduling to prevent one objective from dominating the other"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 12GB+ VRAM for joint training","Careful tuning of loss weights (reconstruction, dynamics, policy, value)"],"input_types":["visual observations","actions","rewards","terminal flags"],"output_types":["learned latent representations","world model parameters","policy parameters","value function parameters"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_6","uri":"capability://image.visual.visual.observation.encoding.with.vae.based.latent.compression","name":"visual observation encoding with vae-based latent compression","description":"DreamerV3 uses a variational autoencoder (VAE) to compress high-dimensional visual observations (e.g., 64x64 RGB images) into a compact latent representation (typically 32-256 dimensions). The encoder network maps observations to a Gaussian distribution in latent space, while the decoder reconstructs observations from latent samples. The VAE is trained with a reconstruction loss (L2 or L1) and a KL divergence regularizer that encourages the latent distribution to match a standard normal prior. This compression enables efficient world model learning and policy optimization in the latent space.","intents":["Compress visual observations into a compact latent space for efficient world modeling and policy learning","Learn disentangled visual representations that separate content (objects, layout) from style (lighting, textures)","Enable world model learning on high-resolution images without prohibitive computational cost"],"best_for":["Visual control tasks with high-dimensional observations (images, video)","Researchers studying representation learning in embodied AI","Teams building efficient RL systems for resource-constrained deployment"],"limitations":["VAE reconstruction loss may blur fine visual details; high-frequency information (edges, textures) is often lost in latent compression","KL divergence regularization can lead to posterior collapse, where the encoder ignores the input and the latent distribution matches the prior","Latent space is not interpretable; debugging visual encoding failures requires analyzing high-dimensional representations","Reconstruction quality is a bottleneck for world model performance; poor reconstruction leads to poor dynamics learning","VAE assumes Gaussian latent distribution; may not be optimal for multi-modal or non-Gaussian visual features"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM","Visual observations (RGB images, 64x64 to 256x256 resolution)"],"input_types":["visual observations (RGB images)","latent dimension (hyperparameter, typically 32-256)"],"output_types":["latent representations (Gaussian distributions)","reconstructed observations","latent samples for world model input"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_7","uri":"capability://planning.reasoning.recurrent.world.model.dynamics.with.gated.recurrent.unit.gru.state.prediction","name":"recurrent world model dynamics with gated recurrent unit (gru) state prediction","description":"DreamerV3 models environment dynamics using a recurrent state-space model where a GRU (gated recurrent unit) network predicts the next latent state given the current latent state and action. The GRU maintains a hidden state that captures temporal dependencies and long-range correlations in the environment dynamics. The model is trained to minimize prediction error on one-step-ahead latent state predictions, enabling efficient amortization of dynamics learning across multiple rollout steps. The recurrent structure enables the model to learn complex temporal patterns (e.g., object momentum, delayed effects) without explicit temporal convolutions.","intents":["Learn environment dynamics that capture temporal dependencies and long-range correlations","Predict future latent states efficiently for imagination-based policy optimization","Enable world model learning on partially observable environments where hidden state is necessary"],"best_for":["Visual control tasks with temporal dependencies (e.g., object momentum, delayed effects)","Partially observable environments where hidden state is necessary for accurate prediction","Researchers studying recurrent world models and temporal reasoning in embodied AI"],"limitations":["GRU hidden state is not interpretable; debugging dynamics prediction failures requires analyzing high-dimensional recurrent states","Recurrent prediction can suffer from error accumulation over long rollouts (>50 steps); errors compound as the model predicts further into the future","GRU training is more computationally expensive than feedforward models due to sequential computation; ~2-3x slower than non-recurrent baselines","Backpropagation through time (BPTT) requires storing intermediate states, increasing memory usage for long rollouts","GRU hidden state initialization is critical; poor initialization can lead to poor dynamics prediction on the first few steps"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM","Latent state representations from VAE encoder"],"input_types":["current latent states","actions","GRU hidden state (initialized from prior or previous step)"],"output_types":["predicted next latent states","updated GRU hidden state","dynamics prediction loss"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_8","uri":"capability://planning.reasoning.value.function.learning.with.two.headed.critic.architecture","name":"value function learning with two-headed critic architecture","description":"DreamerV3 trains a value function using a two-headed critic architecture where one head predicts the value of the current state (critic) and another predicts the target value for bootstrapping (target). Both heads are trained on imagined trajectories using symlog-scaled returns computed from the world model's reward predictions and the target value function. The two-headed design enables stable bootstrapping without explicit target networks or replay buffers. The value function is trained with a Huber loss to reduce sensitivity to outliers in the imagined returns.","intents":["Estimate state values for policy optimization without explicit target networks","Reduce variance in policy gradient estimates by using learned value functions on imagined trajectories","Maintain stable value function learning across diverse reward scales using symlog scaling"],"best_for":["Model-based RL systems where value functions are trained on imagined trajectories","Researchers studying value function design in world-model-based RL","Teams seeking to simplify RL infrastructure by eliminating explicit target networks"],"limitations":["Two-headed critic adds computational overhead (~10-15% per forward pass) compared to single-head design","Value function bootstrapping on imagined states introduces bias if the world model is poorly calibrated","Huber loss requires tuning of the delta parameter; poor choices lead to either high variance (small delta) or biased estimates (large delta)","Value function may overfit to the world model's reward predictions; if the world model's reward predictions are biased, the value function will propagate this bias","No explicit mechanism for value function regularization; may require additional L2 regularization to prevent overfitting"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM","World model with reward prediction head","Imagined trajectories from world model rollouts"],"input_types":["latent states from imagined trajectories","symlog-scaled returns from imagined rollouts"],"output_types":["value function estimates (critic head)","target value estimates (target head)","value function loss"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastering-diverse-domains-through-world-models-dreamerv3__cap_9","uri":"capability://automation.workflow.online.reinforcement.learning.with.world.model.adaptation","name":"online reinforcement learning with world model adaptation","description":"DreamerV3 supports online RL where the world model is continuously updated with new environment interactions, enabling the agent to adapt to changing environments or learn from new data. The process involves: (1) collecting environment interactions using the current policy, (2) adding new transitions to a replay buffer, (3) updating the world model on a mixture of old and new data, and (4) optimizing the policy on imagined rollouts from the updated world model. This enables the agent to discover and adapt to environment changes without retraining from scratch.","intents":["Train agents that adapt to changing environments or new tasks without retraining from scratch","Continuously improve world model quality as more environment data becomes available","Enable lifelong learning where agents accumulate experience over extended periods"],"best_for":["Robotics systems that must adapt to changing environments or new tasks","Researchers studying continual learning and adaptation in embodied AI","Teams building agents that operate in non-stationary environments"],"limitations":["Continuous world model updates can lead to distribution shift; the policy may exploit changes in the world model rather than adapting to environment changes","Replay buffer management is critical; imbalanced data (old vs. new) can lead to poor world model quality or catastrophic forgetting","Online learning introduces additional hyperparameters (replay buffer size, world model update frequency) that must be tuned","No explicit mechanism for detecting environment changes; the agent must infer changes from prediction errors","Computational cost of continuous world model updates adds overhead; ~1.5-2x wall-clock time vs. offline training"],"requires":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 12GB+ VRAM for continuous training","Replay buffer implementation","Environment interaction loop"],"input_types":["new environment transitions (observations, actions, rewards, dones)","replay buffer with historical data"],"output_types":["updated world model parameters","updated policy parameters","updated value function parameters"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ or TensorFlow 2.8+","GPU with 8GB+ VRAM for training on image observations","Environment with visual observations (pixels) or ability to extract visual features","Minimum 10k environment steps for meaningful world model learning","GPU with 12GB+ VRAM for multi-task training","Minimum 50k environment steps across all tasks for meaningful transfer","Consistent visual observation format across all tasks","GPU with 12GB+ VRAM","Access to an LLM (e.g., GPT-3, GPT-4, or open-source alternatives)"],"failure_modes":["World model quality bottlenecks policy performance — errors compound over long imagined rollouts (>50 steps), limiting planning horizon","Requires sufficient diversity in training data to learn generalizable latent representations; fails on out-of-distribution visual inputs","Computational overhead of VAE encoding/decoding and recurrent state prediction adds ~2-5x wall-clock time vs model-free baselines during training","Latent space interpretability is limited; debugging policy failures requires analyzing high-dimensional learned representations","No built-in mechanism for uncertainty quantification in world model predictions, limiting safe exploration in real-world deployment","Task-agnostic world model may not capture task-specific visual features (e.g., subtle object properties relevant only to one task)","Performance degrades when tasks have conflicting visual dynamics or require fundamentally different state representations","Requires careful data balancing across tasks during training; imbalanced task mixtures lead to poor world model quality on underrepresented tasks","No explicit mechanism for task identification or context switching; policies must learn task-specific behavior from shared latent space alone","LLM-generated reward functions may be misaligned with intended objectives; 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