RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System

Yinjie Wang, Tianbao Xie, Ke Shen +2

We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, amplifying learning signa…