Proposes a new reinforcement learning policy compression method based on long-horizon state-space coverage instead of immediate action-matching.
March 31, 2026
Original Paper
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
arXiv · 2603.27044
The Takeaway
Traditional behavior cloning suffers from compounding errors because it only matches local actions. This approach organizes the latent space around true functional similarity (occupancy), leading to more robust and generalized policy manifolds that represent actual behavior rather than just output tokens.
From the abstract
Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space. A recent framework, which we refer to as Action-based Policy Compression (APC), mitigates this issue by compressing the parameter space $\Theta$ into a low-dimensional latent manifold $\mathcal Z$ using a learned generative mapping $g:\mathcal Z \to \Theta$. However, its performance