What Drives Interactive Improvement from Feedback?
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 t
By Bart{\l}omiej Cupia{\l}, Jan {\L}ojek, Miko{\l}aj Garstecki, Szymon Pob{\l}ocki, Alicja Ziarko, Piot