Language Model Backbone Size Effects on RxR Agent Path Efficiency and Communication Success

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of varying the size of the language model backbone on the path efficiency and communication success rate of RxR-trained agents in the R2R benchmark. Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of varying the size of the language model backbone on the path efficiency and communication success rate of RxR-trained agents in the R2R benchmark?

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