DALI: Dynamics-Aligned Latent Imagination

• The paper introduces DALI, which augments the Dreamer architecture with a self-supervised context encoder to infer latent vectors that align with true environmental dynamics.
• DALI is a model-based reinforcement learning framework that infers actionable context from short histories, enabling efficient policy learning and zero-shot adaptation in dynamic environments.
• The method achieves robust generalization and counterfactual consistency, with empirical gains up to 96.4% in extrapolation tasks, reducing sample complexity compared to conventional models.

Dynamics-Aligned Latent Imagination (DALI) is a paradigm for model-based reinforcement learning that centers on constructing latent state spaces capable of supporting imagined rollouts which are tightly coupled to the true underlying environment dynamics. Its methodological focus is to infer actionable, structured latent variables—often from high-dimensional observations—so that policy learning, planning, and generalization are achieved by simulating future states in this latent space. DALI provides a systematic framework for robust zero-shot adaptation to latent and continuously varying context factors, particularly in contextual Markov Decision Processes (cMDPs) encountered in real-world reinforcement learning.

1. DALI Framework and Integration Strategies

DALI is realized by augmenting the foundational Dreamer architecture with a self-supervised context encoder that infers latent representations of environmental context from short histories of agent–environment transitions (Röder et al., 27 Aug 2025). The framework supports two principal forms of integration:

• Shallow Integration: The inferred context vector $\hat c_t$ is appended to observation embeddings and input to the encoder,

$z_t \sim q_\theta(z_t \mid h_t, o_t, \hat c_t)$

where $h_t$ is the recurrent state, $o_t$ the raw observation, and $z_t$ the stochastic latent variable.

• Deep Integration: The context representation conditions the recurrent dynamics function throughout the world model,

$$h_t = f_\theta(h_{t-1}, z_{t-1}, a_{t-1}, \hat c_t)$$

with all state and reward predictions, as well as policy outputs, conditioned on $\hat c_t$.

The self-supervised encoder $g_\varphi$ consumes a short window (length $K$) of $(o_{t-K:t}, a_{t-K:t-1})$ to produce $\hat c_t$, optimized via a forward dynamics alignment loss,

$$\mathcal{L}_{\text{FD}}(\varphi) = \mathbb{E}\big\| o_{t+1} - f_\varphi^w(o_t, a_t, \hat c_t)\big\|_2^2$$

which enforces that $\hat c_t$ is informative for predicting physical evolution. Additional cross-modal regularization aligns $z_t$ and $\hat c_t$ bi-directionally.

2. Inference of Latent Context Representation

A core technical advance in DALI is the direct inference of context from interactions, without explicit supervision. The context encoder $g_\varphi$ is trained end-to-end to encode environmental factors (e.g., gravity, friction, mass) as compact vectors $\hat c_t$, optimizing prediction of future states:

$\hat c_t = g_\varphi(o_{t-K:t}, a_{t-K:t-1})$

Subject to the forward dynamics alignment loss, the encoder converges to representations which encapsulate those environmental attributes most relevant for actionable decision-making and world model accuracy.

By design, the context encoder bridges perception and control, since $\hat c_t$ serves as conditioning for both imaginative rollouts in the world model and direct policy adaptation.

3. Theoretical Guarantees and Sample Complexity

The efficacy of short-window context inference is substantiated by rigorous analysis (Röder et al., 27 Aug 2025). Under assumptions of Lipschitz-continuous dynamics w.r.t. context, and $\beta$-mixing processes for observations, it is proven that a window of $K$ transitions suffices for near-optimal context inference:

$\mathcal{I}(c; \hat c_t) \geq (1-\delta) h(c)$

for arbitrarily small $\delta > 0$, with $K = \Omega(\log(1/\delta)/\lambda)$ where $\lambda$ is the mixing rate. This dramatically reduces the information bottleneck compared to episode-length recurrent architectures, with sample complexity gains scaling as $\mathcal{O}(T/K)$.

These results demonstrate that decoupling context inference from dynamics prediction yields both efficient generalization and robustness to varying or latent environmental factors—capabilities unattainable by conventional Dreamer variants which overload the recurrent state for both history and context.

4. Counterfactual Consistency and Latent Space Analysis

Empirical analysis reveals that DALI’s latent space exhibits counterfactual consistency: perturbing individual dimensions of $\hat c_t$ that encode environmental parameters (e.g., gravity) leads to physically plausible changes in imagined trajectories. In the Ball-in-Cup domain, increasing the value of the gravity-encoding dimension $\hat c_6$ results in the ball falling faster and swinging on a shorter string, with quantitative metrics (object position, velocity) matching Newtonian expectations.

Such counterfactual perturbations substantiate that the learned latent context variables are physically interpretable and operationally actionable—they support rollouts that genuinely reflect altered environmental conditions, with implications for model-based planning, control, and scientific understanding.

5. Empirical Evaluation and Zero-Shot Generalization

DALI’s empirical performance is benchmarked on challenging cMDP tasks (Röder et al., 27 Aug 2025), including DeepMind Control Suite (Ball-in-Cup, Walker Walk) with context variations over gravity, string length, actuator strength, and more. Results are summarized as follows:

Regime	DALI (zero-shot)	Context-unaware (DreamerV3)	Context-aware baselines
Interpolation	High	Moderate–High	High
Extrapolation	87.9–96.4% gain	Markedly lower	33.8–63.9% gain
Mixed	Robust	Variable/falling	Robust

In extrapolation, DALI variants (including DALI-S with cross-modal regularization) outperform both context-unaware and explicit context-aware architectures, demonstrating successful adaptation to out-of-distribution conditions. The agent infers and exploits context representations for control, without access to ground-truth environmental parameters.

6. Real-World Applications and Broader Impact

DALI addresses core requirements of real-world reinforcement learning where adaptation to unknown, latent, or time-varying environmental conditions is paramount and direct measurement of context is infeasible. Applications include:

• Robotic manipulation in environments with uncertain friction or mass.
• Autonomous control in settings with evolving obstacles or system dynamics.
• Sim2real transfer where context mismatch challenges conventional model reliance.

DALI’s approach of learning implicit, physically grounded context representations and dynamically aligning the world model and policy to these latent variables enables robust zero-shot generalization. The reduction of the information bottleneck and sample complexity requirements allows agents to adapt without costly retraining, making DALI especially relevant for practical robotics, adaptive control, and scientific modeling.

7. Conclusion

Dynamics-Aligned Latent Imagination integrates self-supervised context encoding into world models so that imagined trajectories remain physically consistent with environmental dynamics, even under latent or unseen context variations. The approach is mathematically supported by bounds on mutual information and sample complexity and empirically validated on diverse control tasks. Counterfactual consistency and robust zero-shot generalization position DALI as a powerful paradigm for adaptive, model-based reinforcement learning in real-world settings where context inference is intrinsic and retraining expensive or impractical.