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cs.AI2026
What Drives Interactive Improvement from Feedback?
Bartłomiej Cupiał, Jan Łojek, Mikołaj Garstecki +3
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 c…
cs.LG2026
On the Role of Computation in Reinforcement Learning
Raj Ghugare, Michał Bortkiewicz, Alicja Ziarko +1
How does the amount of compute available to a reinforcement learning (RL) policy affect its learning? Can policies using a fixed amount of parameters, still benefit from additional…