PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning.

arXiv:2609.31855v1 Announce Type: cross Abstract: Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rather than direct policy guidance