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Shockingly Simple Self-retrospection Improves Agentic Models Without RL

Paper recorded by Signals 4 on 2026-09-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.AI · 人工智能 · first seen 2026-09-29

Abstract

People learn not only by repeating successful actions, but also by recounting and explaining their experiences, revising their understanding to guide future behavior. Can a language-model agent improve its future actions by training only on explanations of its own experience? We investigate this question by studying Retrospection-Only Fine-Tuning (ROFT), a minimal online procedure designed to isol

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#9 most recent of 440 cs.AI papers we have recorded · ↑ newer: FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluatin · ↓ older: Failure-Transparent Agents: Benchmarking Post-Failure Reporting in Too

Cite this page: Shockingly Simple Self-retrospection Improves Agentic Models Without RL: the #9 most recent of 440 cs.AI papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/shockingly-simple-self-retrospection-improves-agentic-models-without-rl.html
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