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From Parameters to Behaviors: Unsupervised Compression of the Policy Space

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
reinforcement learningunsupervised reinforcement learningunsupervised representation learning
Abstract:

Despite its recent successes, Deep Reinforcement Learning (DRL) is notoriously sample-inefficient. We argue that this inefficiency stems from the standard practice of optimizing policies directly in the high-dimensional and highly redundant parameter space Theta\\Theta. This challenge is greatly compounded in multi-task settings. In this work, we develop a novel, unsupervised approach that compresses the policy parameter space Theta\\Theta into a low-dimensional latent space mathcalZ\\mathcal Z. We train a generative model g:mathcalZtoThetag:\\mathcal Z\\to\\Theta by optimizing a behavioral reconstruction loss, which ensures that the latent space is organized by functional similarity rather than proximity in parameterization. We conjecture that the inherent dimensionality of this manifold is a function of the environment's complexity, rather than the size of the policy network. We validate our approach in continuous control domains, showing that the parameterization of standard policy networks can be compressed up to five orders of magnitude while retaining most of its expressivity. As a byproduct, we show that the learned manifold enables task-specific adaptation via Policy Gradient operating in the latent space mathcalZ\\mathcal{Z}.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes unsupervised compression of policy parameter space into a low-dimensional latent space using a generative model trained via behavioral reconstruction loss. It resides in the 'Behavioral Reconstruction-Based Compression' leaf, which contains only two papers total (including this one). This leaf sits within the broader 'Direct Policy Parameter Space Compression' branch, indicating a relatively sparse research direction. The taxonomy shows that most related work focuses on skill discovery or state representation learning rather than direct parameter compression, suggesting this approach occupies a less crowded niche within the reinforcement learning compression landscape.

The taxonomy reveals neighboring branches emphasizing different compression targets. 'Unsupervised Skill Discovery and Behavioral Primitives' learns reusable action sequences through diversity objectives or mutual information, while 'State and Trajectory Representation Learning' compresses observations or rollouts rather than policy weights. The paper's focus on parameter-to-behavior mapping distinguishes it from these alternatives. The scope notes clarify that behavioral reconstruction methods organize latent space by functional similarity, not parameter proximity, differentiating this work from general autoencoder approaches that lack behavioral grounding. This positioning suggests the paper bridges parameter-level efficiency with behavior-level interpretability.

Among thirty candidates examined, the contribution-level analysis shows mixed novelty signals. The core compression idea (Contribution 1) found one refutable candidate among ten examined, indicating some prior overlap in the limited search scope. The behavioral reconstruction loss (Contribution 2) showed no refutations across ten candidates, suggesting greater novelty within the examined literature. The two-stage framework (Contribution 3) also encountered one refutable candidate among ten. These statistics reflect a targeted semantic search, not exhaustive coverage, meaning additional related work may exist beyond the top-thirty matches analyzed here.

Based on the limited search scope, the work appears moderately novel in its specific combination of parameter compression and behavioral reconstruction. The sparse taxonomy leaf and mixed refutation statistics suggest the approach occupies a distinct but not entirely unexplored position. The analysis covers top-thirty semantic matches and does not claim completeness; broader literature may reveal additional connections not captured in this focused examination.

Taxonomy

Core-task Taxonomy Papers
17
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Unsupervised compression of policy parameter space into low-dimensional behavioral manifolds. The field addresses how to distill high-dimensional policy representations into compact, interpretable structures that capture meaningful behavioral variation. The taxonomy reveals four main branches. Direct Policy Parameter Space Compression methods work directly on policy weights or parameters, seeking low-dimensional embeddings that preserve behavioral similarity. Unsupervised Skill Discovery and Behavioral Primitives approaches learn reusable action sequences or skills without supervision, often through diversity-driven objectives or mutual information maximization, as seen in works like Metra[5] and Skill Regions Differentiation[11]. State and Trajectory Representation Learning focuses on compressing observations or rollout data rather than policy parameters themselves, using techniques such as slow feature analysis (Slow Features RL[7]) or outcome-based embeddings (Reachable Outcome Space[9]). General Autoencoder-Based Compression and Dimensionality Reduction encompasses broader methods that apply variational or standard autoencoders to various representations, including features (Autoencoder Feature Learning[15]) and correlative subspaces (Correlative Subspace Learning[16]). A central tension across these branches is whether to compress the policy parameter space directly or to instead compress the induced behaviors, trajectories, or outcomes. Direct parameter compression can be efficient for transfer and storage but may struggle to capture behavioral nuance when parameter perturbations yield complex nonlinear effects. In contrast, behavior-centric methods like Trajectory Clustering Modes[6] or Multi-modal Skill Memories[12] offer richer semantic structure but require rollout data and can be computationally expensive. Parameters to Behaviors[0] sits within the Direct Policy Parameter Space Compression branch, specifically under Behavioral Reconstruction-Based Compression, closely related to Parameters to Behavior[4]. Both emphasize reconstructing observable behaviors from compressed parameter representations, balancing the efficiency of parameter-level encoding with the interpretability of behavior-level metrics. This approach contrasts with purely skill-discovery methods that do not explicitly model parameter space, and with general autoencoders that may lack behavioral grounding.

Claimed Contributions

Unsupervised compression of policy parameter space into low-dimensional latent behavior space

The authors propose a method to compress high-dimensional policy parameter spaces into compact latent representations organized by behavioral similarity rather than parameter proximity. This compression is achieved through a generative model trained with a behavioral reconstruction loss in a task-agnostic manner.

10 retrieved papers
Can Refute
Behavioral reconstruction loss for training generative models

The authors introduce a novel training objective that minimizes behavioral divergence between original and reconstructed policies rather than parameter reconstruction error. This ensures the learned latent space captures functional similarity of policies instead of parameter-level proximity.

10 retrieved papers
Two-stage framework for unsupervised pre-training and supervised fine-tuning in latent space

The authors develop a modular pipeline consisting of unsupervised pre-training to discover the behavioral manifold followed by supervised fine-tuning via policy gradient methods operating in the learned low-dimensional latent space. This enables efficient task-specific adaptation without learning from scratch.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unsupervised compression of policy parameter space into low-dimensional latent behavior space

The authors propose a method to compress high-dimensional policy parameter spaces into compact latent representations organized by behavioral similarity rather than parameter proximity. This compression is achieved through a generative model trained with a behavioral reconstruction loss in a task-agnostic manner.

Contribution

Behavioral reconstruction loss for training generative models

The authors introduce a novel training objective that minimizes behavioral divergence between original and reconstructed policies rather than parameter reconstruction error. This ensures the learned latent space captures functional similarity of policies instead of parameter-level proximity.

Contribution

Two-stage framework for unsupervised pre-training and supervised fine-tuning in latent space

The authors develop a modular pipeline consisting of unsupervised pre-training to discover the behavioral manifold followed by supervised fine-tuning via policy gradient methods operating in the learned low-dimensional latent space. This enables efficient task-specific adaptation without learning from scratch.

Table of Contents

From Parameters to Behaviors: Unsupervised Compression of the Policy Space | Novelty Validation