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Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts research paper by Microsoft, 2026

Microsoft · Jun 4, 2026 · Agents and evaluation · 10 citations · 36 upvotes · unverified

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What it shows

Retrospective Harness Optimization (RHO) is a self-supervised method that improves AI agent performance by optimizing agent harness using only past trajectories through diverse task selection, parallel re-solving, and self-validation techniques.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Wenbo Pan, Shujie Liu, Chin-Yew Lin and 5 more
arXiv
2606.05922 · PDF
Venue
arXiv.org
Citations
10, 1 influential · Semantic Scholar
Upvotes
36 · Hugging Face
Code
github.com/wbopan/retro-harness
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 1Sep 25, 2026
Citationsfirst count: 10Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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