The granularity of emergent temporal abstractions is correlated with local epistemic uncertainty, and dynamically modulating the abstraction level during inference improves robustness in stochastic environments.

Motivation

The paper likely assumes a fixed or implicitly learned hierarchy. However, in stochastic environments, an agent should plan at a high level when confident but revert to low-level motor control when facing uncertainty. Validating this correlation and exploiting it could create agents that are robust to perturbations without explicit retraining.

Proposed Method

1. Train the autoregressive hierarchical model as described in the paper. 2. Introduce a runtime mechanism that monitors the entropy of the next-token prediction (uncertainty). 3. Implement an adaptive 'stride' or hierarchy selector: when entropy is low, the model operates on high-level temporal abstractions (skipping steps); when entropy spikes, it forces step-by-step low-level execution. 4. Test in environments with random noise injection or domain shifts.

Expected Contribution

Evidence that emergent abstractions can be dynamically sized for robust control, establishing a link between model uncertainty and optimal hierarchical depth.

Required Resources

Standard ML setup; custom RL environments with adjustable stochasticity.

Source Paper

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning

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