[Submitted on 29 Jun 2026]
Title:What Drives Interactive Improvement from Feedback?
View a PDF of the paper titled What Drives Interactive Improvement from Feedback?, by Bart{\l}omiej Cupia{\l} and 5 other authors
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Abstract:We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test-time computation. To separate these effects, we introduce a controlled student-teacher protocol across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1, evaluating thirteen open-weight models in both student and teacher roles. We compare external feedback, self-feedback, and unguided self-refinement, while varying interaction history, task difficulty, and teacher access to privileged task information. Across settings, we find that multi-turn improvement is often not evidence of feedback use: self-generated feedback adds little beyond unguided self-refinement, whereas the strongest external teachers produce substantially larger feedback-specific gains, suggesting that useful feedback must provide guidance beyond generic retry. Dense student-teacher interaction matrices further show that interactive gains are driven more by the student's ability to use feedback than by the teacher's identity, although teacher choice remains important for a fixed student. These results suggest that feedback-based agents should be evaluated against repeated-attempt baselines, and that ability to act on feedback, not merely feedback availability, is a central bottleneck for interactive improvement. We release our controlled student-teacher evaluation framework at this https URL.
Comments: 9 pages, 7 figures, accepted to the RLxF Workshop at ICML 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30774 [cs.AI]
(or arXiv:2606.30774v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30774
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bartłomiej Cupiał [view email] [v1] Mon, 29 Jun 2026 18:06:36 UTC (1,801 KB)
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