31 citations · 72 across the 22 of their papers we have counts for
3 papers · 1 filter
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning
Akash Velu, Skanda Vaidyanath, Dilip Arumugam
Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme…
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
Wanqiao Xu, Shi Dong, Dilip Arumugam +1
A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward mode…
Bayesian Reinforcement Learning with Limited Cognitive Load
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be ab…