2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Learning Embeddings for Sequential Tasks Using Population of Agents
Mridul Mahajan, Georgios Tzannetos, Goran Radanovic +1
We present an information-theoretic framework to learn fixed-dimensional embeddings for tasks in reinforcement learning. We leverage the idea that two tasks are similar if observin…
Neural Task Synthesis for Visual Programming
Victor-Alexandru Pădurean, Georgios Tzannetos, Adish Singla
Generative neural models hold great promise in enhancing programming education by synthesizing new content. We seek to design neural models that can automatically generate programm…
Proximal Curriculum for Reinforcement Learning Agents
Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban +1
We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically…