Learning by Doing: How Actions Unlock Better AI Understanding

Author: Denis Avetisyan

A new framework demonstrates that embedding actions within machine learning models improves their ability to learn and interpret complex systems.

The system considers how a physical entity, defined by a set of varying factors [latex]\mathbf{c}[/latex], responds to applied actions [latex]a\_{i} \in \mathbb{A}[/latex], where outcomes [latex]y\_{i}(\mathbf{c})[/latex] depend only on subsets of those factors, and demonstrates that any disentanglement of shared factors-like [latex]c\_{2}[/latex] required by multiple actions-will be achieved through a variational autoencoder architecture with separate encoders [latex]E\_{X}[/latex] and [latex]E\_{A}[/latex] processing input samples and action combinations to inform a shared decoder [latex]D[/latex] and ultimately predict system outputs.

This review introduces Action-Induced Representations (AIR) and a corresponding Variational Autoencoder (VAIR) architecture for improved disentangled representation learning and physical system identification.

Achieving fully interpretable representations remains a central challenge in modern machine learning, particularly when employing latent variable models. This paper, ‘Disentanglement by means of action-induced representations’, introduces a novel framework-Action-Induced Representations (AIR)-that fundamentally links representation learning to the experimental manipulations performed on a physical system. By explicitly modeling how actions influence underlying generative factors, the authors demonstrate provable disentanglement via a variational architecture (VAIR) where standard variational autoencoders often fail. Could this action-centric approach unlock a new paradigm for building more robust and causal models of complex systems?

The Illusion of Control: Unraveling Latent Variables

Machine learning frequently demands an understanding of the generative forces shaping observed data – the underlying factors of variation that account for differences in appearance, style, or context. However, when algorithms attempt to compress high-dimensional data into lower-dimensional “latent spaces”, these factors often become intertwined, creating what is known as entanglement. This entanglement hinders interpretability; instead of each dimension in the latent space representing a single, meaningful attribute – like an object’s pose or lighting – it becomes a complex mixture, making it difficult to isolate and control specific characteristics. Consequently, models trained on entangled latent spaces struggle to generalize effectively, impacting performance on downstream tasks that require manipulation or understanding of these underlying factors. The challenge, therefore, lies in developing methods that can learn disentangled representations, where each latent dimension corresponds to an independent and interpretable source of variation within the data.

Conventional autoencoders, while effective at dimensionality reduction, often fail to create truly disentangled representations, leading to limitations in practical applications. The core issue lies in their tendency to distribute information about multiple underlying factors across several latent dimensions; instead of a single dimension controlling, for example, the pose of an object in an image, that information is spread throughout the latent space. This entanglement hampers the model’s ability to generalize to unseen data, as subtle changes in the input can produce unpredictable variations in the reconstructed output. Consequently, performance on downstream tasks – such as image editing, anomaly detection, or transfer learning – suffers because the model cannot reliably manipulate individual factors of variation without affecting others, diminishing its utility in complex scenarios requiring precise control and interpretable representations.

A central ambition in representation learning involves constructing latent spaces where individual dimensions capture distinct and independent aspects of the data generating process. This disentanglement isn’t merely about interpretability; it’s about building more robust and generalizable models. When a latent dimension cleanly corresponds to a single source of variation – such as an object’s pose, lighting, or style – the model can manipulate that specific attribute without affecting others. Consequently, interventions within the latent space become more predictable and controlled, improving performance on downstream tasks like image editing, data generation, and transfer learning. Achieving this ideal requires innovative architectural designs and training procedures that encourage the emergence of such factorized representations, moving beyond the limitations of traditional autoencoders that often produce tangled and less meaningful latent variables.

Analysis of latent neuron behavior reveals that variance [latex]\sigma_i[/latex] and mean [latex]\mu_i[/latex] evolve during training and correlate with hidden factors [latex]c_i[/latex], with the mutual information gap demonstrating the importance of a specific subset of factors [latex]c_2[/latex] for representation learning.

VAIR: Action as the Seed of Disentanglement

VAIR, or Variational Action-induced Representation, is a novel variational autoencoder (VAE) architecture engineered to learn action-induced representations (AIR). Unlike standard VAEs which focus on general data encoding, VAIR specifically targets the extraction of latent factors directly correlated with agent actions within an environment. The architecture is built upon the principles of variational inference, utilizing an encoder-decoder structure to map observations to a probabilistic latent space and reconstruct the original data. By framing the representation learning problem around actions, VAIR aims to create latent variables that are explicitly tied to behavioral control, facilitating interpretability and enabling downstream tasks such as action prediction and planning.

VAIR’s training process is governed by the Evidence Lower Bound (ELBO), a loss function comprising two primary terms: Reconstruction Loss and KL Divergence. Reconstruction Loss quantifies the difference between the input data and its reconstruction from the latent representation, ensuring the model accurately captures the essential features of the observed data. KL Divergence, conversely, measures the dissimilarity between the learned latent distribution and a prior distribution – typically a standard normal distribution – encouraging the latent space to be well-structured and preventing overfitting. The ELBO, expressed as [latex]ELBO = E_{q(z|x)}[log p(x|z)] – KL(q(z|x) || p(z))[/latex], is maximized during training, effectively balancing reconstruction fidelity with latent space regularization.

VAIR promotes the development of disentangled and interpretable representations by establishing a direct correspondence between latent factors and the actions taken during experimentation. This linkage is achieved through the model’s architecture and training process, which encourages specific latent dimensions to encode information relevant to particular actions. Consequently, changes in a single latent factor predictably modulate the corresponding action, and vice versa, facilitating isolation of causal relationships and improving the interpretability of the learned representations. This explicit action-linking contrasts with traditional variational autoencoders where latent factors often represent entangled and less readily interpretable features.

Variational autoencoder benchmarks, including time-continuous (TC-VAE) and standard (VAE) architectures, were evaluated with and without action input to the decoder ([latex]VAED_{D_{a}}[/latex] and [latex]TC-VAED_{D_{a}}[/latex]), demonstrating performance variability across 20 independent training runs.

Measuring the Shadows: Quantifying Disentanglement with MIG

The Mutual Information Gap (MIG) is utilized as a quantitative metric for evaluating the degree of disentanglement in latent variable models. MIG measures the difference between the total variation distance of the latent variable’s distribution and the total variation distance of the conditional distribution given the observed data; a higher MIG score indicates greater independence between the latent variables. Specifically, [latex]MIG = \sum_{i} TV(p(z_i) || p(z_i | x))[/latex], where [latex]z_i[/latex] represents the i-th latent variable, [latex]x[/latex] represents the observed data, and TV denotes the total variation distance. This metric effectively assesses whether each latent variable contains independent and meaningful information, separate from other latent variables and the input data.

Comparative analysis was conducted utilizing standard Variational Autoencoders (VAEs) and Total Correlation VAEs (TC-VAEs) as baselines to evaluate VAIR’s performance. TC-VAEs incorporate a Total Correlation Loss term into the loss function, explicitly penalizing dependencies between the latent variables to encourage a more disentangled representation. This approach aims to minimize the mutual information between latent dimensions, promoting independence and interpretability. The performance of VAIR was then benchmarked against these models across a series of experiments to quantify the relative effectiveness of each architecture in achieving disentanglement, utilizing metrics such as the Mutual Information Gap [latex] MIG [/latex].

Quantitative evaluation of the Variational Information Bottleneck for Representation learning (VAIR) consistently yielded higher Mutual Information Gap (MIG) scores in abstract experimentation compared to standard Variational Autoencoders (VAEs) and Total Correlation VAEs (TC-VAEs). The MIG metric quantifies disentanglement by measuring the gap between the mutual information shared between individual latent variables and their joint distribution with observed data; higher scores indicate greater independence between latent factors. Specifically, VAIR demonstrated a statistically significant improvement in MIG across multiple datasets, confirming its ability to learn more disentangled representations as defined by this metric. These results suggest that the information bottleneck principle, as implemented in VAIR, effectively encourages the learning of independent and interpretable latent features.

The benchmark compares two Variational Autoencoder (VAE) architectures-TC-VAE and VAE-which differ in their input/output representations ([latex]x_x[/latex] and [latex]y_y[/latex]), and explores variants incorporating action input to the decoder (VAE[latex]D_a[/latex] and TC-VAE[latex]D_a[/latex]).

Beyond Prediction: Disentanglement as a Key to Understanding

Accurate trajectory reconstruction hinges on the ability to isolate and independently represent the underlying factors governing a system’s evolution, a feat effectively achieved through disentangled representations learned by the Variational Information Bottleneck for Reinforcement Learning (VAIR) framework. This approach moves beyond simply memorizing past states by instead learning a compressed, interpretable latent space where each dimension corresponds to a distinct aspect of the dynamics-such as position, velocity, or external forces. By decoupling these factors, VAIR allows for more robust predictions, even when faced with noisy or incomplete data, as the model can generalize based on the fundamental principles at play rather than being overly reliant on specific observed trajectories. The efficacy of this technique extends beyond traditional physics-based simulations; it has proven valuable in reconstructing complex quantum states, effectively revealing latent variables analogous to classical physical properties like mass and charge, thereby demonstrating its broad applicability across diverse scientific domains.

The capacity to accurately forecast future states hinges on a system’s ability to isolate and independently model the underlying factors driving its evolution. Recent advancements demonstrate that decoupling these influential elements-such as mass, charge, or environmental conditions-from the overall dynamic significantly enhances predictive performance. This approach fosters robustness by preventing interference between factors; changes in one element no longer introduce noise or error into the modeling of others. Consequently, predictions become more stable and reliable even when faced with complex, high-dimensional data or inherent uncertainties within the system. This disentangled representation allows for targeted intervention and control, as modifications to a single factor can be predicted and managed with greater precision, leading to more effective and accurate trajectory reconstruction.

The versatility of Variational Information Bottleneck Representation (VAIR) extends beyond typical trajectory prediction, proving remarkably effective in reconstructing complex systems like quantum states through Quantum Tomography. In this application, VAIR doesn’t just predict; it learns the underlying structure – specifically, the Bloch representation of a two-qubit state. This learning process yields interpretable latent variables, mirroring fundamental physical properties; researchers observed that these variables correspond directly to mass and charge as measured in a parallel classical physics experiment. This successful recovery of meaningful physical parameters demonstrates VAIR’s ability to not only model dynamics but also to extract and represent inherent, physically relevant information within the data, opening avenues for its application in scientific discovery across diverse fields.

This quantum experiment demonstrates that a single neuron can encode each action, allowing for prediction of measurement outcomes based on action-specific responses and revealing a logarithmic scaling relationship [latex]\log(\sigma^{2}) \propto -v^{(n)}[/latex] between output variance and tuning parameter [latex]v^{(n)}[/latex].

The pursuit of disentangled representations, as demonstrated by this work on Action-Induced Representations, isn’t merely about achieving cleaner data; it’s about acknowledging the inherent messiness of systems. Monitoring, in this context, becomes the art of fearing consciously, anticipating the inevitable revelations that emerge when a model encounters the unpredictable. The framework doesn’t build understanding, it grows it, coaxing forth interpretable structures from the interplay of action and observation. As Bertrand Russell observed, “The whole problem with the world is that fools and fanatics are so confident in their own opinions.” This confidence, absent in a truly resilient system, is replaced by an acceptance of uncertainty – true resilience begins where certainty ends, and the AIR architecture embodies this principle by embracing the complexity of physical systems.

What’s Next?

The pursuit of disentangled representations, as exemplified by Action-Induced Representations, invariably reveals not a solution, but a shifting of dependencies. The system is not simplified; its failure modes become more subtly interconnected. One splits the observable variables, believing one has isolated causal threads, yet the underlying fragility remains, a latent vulnerability awaiting a novel perturbation. This work demonstrates enhanced interpretability, but interpretability is merely the illusion of control, a map drawn over terrain that continues to reshape itself.

Future iterations will undoubtedly focus on scaling these architectures, applying them to increasingly complex systems. However, increased complexity does not equate to increased robustness. Each added layer of abstraction introduces further potential for unforeseen consequences. The very act of modeling a physical system, of extracting ‘representations’, subtly alters that system, introducing a new form of dependency – a dependency on the model itself.

The challenge, then, is not simply to build more elegant disentanglement algorithms. It is to acknowledge that all systems tend toward entanglement, toward a unified state of failure. Perhaps the true progress lies not in attempting to avoid this fate, but in designing systems that gracefully accommodate it, systems that fail predictably, and with minimal collateral damage.

Original article: https://arxiv.org/pdf/2602.06741.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Illusion of Control: Unraveling Latent Variables

VAIR: Action as the Seed of Disentanglement

Measuring the Shadows: Quantifying Disentanglement with MIG

Beyond Prediction: Disentanglement as a Key to Understanding

What’s Next?

See also: