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MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors

Paper recorded by Signals 4 on 2026-10-26 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.CL · 自然语言处理 · first seen 2026-10-27

Abstract

Error-targeted learning gives a legible way to improve a model: identify specific weaknesses of the model, train the model to fix these weaknesses, and check whether the weaknesses are fixed. We study how to apply error-targeted learning to terminal agents, which solve long-horizon tasks specified in natural language by interacting with a command shell. Existing error-targeted methods are designed

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#141 most recent of 660 cs.CL papers we have recorded · ↑ newer: From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loo · ↓ older: Not One Belief: Answer Payloads, Persistence, and Dissociated Confiden
Cite this page: MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors: the #141 most recent of 660 cs.CL papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mirl-error-targeted-reinforcement-learning-for-correcting-terminal-agent-misbeha.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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