This problem setting is motivated by the successful deep Q-networks (DQN) framework that falls in this regime.

To mitigate this gap, we first refine and extend the definition of honesty inLLMs based onthedefinition proposed byAskelletal.[14],astheability

Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories. Few-shot learning is a promising solution for alleviating this issue by allowing robots to perform a few interactions with unseen objects.

The Power Descent, defined for allα = 1, is one such algorithm and we establish in our work the full proof ofits convergence towards the optimal mixture weights whenα < 1.