Continuous Latent Action Models vs. Supervised Contrastive Learning on BridgeData V2 Under Noisy Demonstrations

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the policy success rate of CLAM's continuous latent action models compare to supervised contrastive learning when evaluated on the BridgeData V2 benchmark with varying levels of noise in the. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the policy success rate of CLAM's continuous latent action models compare to supervised contrastive learning when evaluated on the BridgeData V2 benchmark with varying levels of noise in the unlabeled demonstrations?

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