AI RESEARCH
Dynamical Priors as a Training Objective in Reinforcement Learning
arXiv CS.LG
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ArXi:2604.21464v1 Announce Type: new Standard reinforcement learning (RL) optimizes policies for reward but imposes few constraints on how decisions evolve over time. As a result, policies may achieve high performance while exhibiting temporally incoherent behavior such as abrupt confidence shifts, oscillations, or degenerate inactivity. We