Venue: Bharti-501/ MS Teams Abstract: In-Context Learning (ICL) is a remarkable phenomenon whereby transformer-based LLMs can use data contained within their prompts to adapt their responses, without changing their weights. This suggests that such models encode learning mechanisms. The recent literature has used statistical learning problems as a test-bed to investigate ICL, and established that ICL can… Read More »Algorithmic Behaviours in In-Context Learning by Dr. Aditya Gangrade

Venue: SIT001 Abstract: Solving long-horizon tasks remains a central challenge in robotics because agents must explore efficiently, assign credit over long time scales, and act under sparse supervision. To address this, hierarchical reinforcement learning (HRL) offers a promising alternative to flat reinforcement learning (RL) by enabling a high-level policy to propose subgoals and a low-level… Read More »Learning Hierarchical Control via Feasible Subgoal Prediction by Utsav Singh