A research paper introduces REVERSAL-BENCH, a benchmark for measuring the reset-free RL cliff in autonomous reinforcement learning. The benchmark controls reversibility via a continuous parameter and provides a reset oracle to test state recoverability. Evaluations of various policy architectures reveal a sharp reversibility cliff, with reset-free agents failing as irreversibility increases.