Continuous Latent Action Models Outperform Discrete Models in Heterogeneous Demonstration Learning

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does the sample efficiency of continuous latent action models compare to discrete action models when trained on heterogeneous demonstration datasets (mixed human and simulated trajectories) and. 12 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the sample efficiency of continuous latent action models compare to discrete action models when trained on heterogeneous demonstration datasets (mixed human and simulated trajectories) and evaluated on the Meta-World Door Open task using the same number of environment interactions?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance