3 papers
cs.LG2025
Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs
Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
Training agents to operate under strict constraints during deployment, such as limited resource budgets or stringent safety requirements, presents significant challenges, especiall…
cs.LG2024
Proximal Curriculum with Task Correlations for Deep Reinforcement Learning
Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
Curriculum design for reinforcement learning (RL) can speed up an agent's learning process and help it learn to perform well on complex tasks. However, existing techniques typicall…
cs.LG2024
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +3
In this paper, we take a step towards a deeper understanding of learning from human preferences by systematically comparing the paradigm of reinforcement learning from human feedba…