Differentiable Evolutionary Reinforcement Learning

ArXi:2512.13399v2 Announce Type: replace Crafting effective reward signals remains a central challenge in Reinforcement Learning (RL), especially for complex reasoning tasks. Existing automated reward optimization methods typically rely on derivative-free search heuristics that treat the reward function as a black box, failing to exploit the causal dynamics between reward structure modifications and policy performance. We