Training Signal Collapse Mitigation in Reinforcement Learning

Abstract

Training signal collapse represents a critical failure mode in reinforcement learning, in which reward
gradients weaken to the point that policy updates no longer support meaningful learning progression.
This study investigates the underlying causes of signal collapse, including sparse reward structures,
exploration decay, unstable policy update magnitudes, and credit assignment challenges across long
temporal horizons. A structured stabilization methodology was applied, incorporating bounded policy
updates, adaptive exploration control, reward scaffolding, curriculum progression, and hierarchical
action abstraction. Experimental results show that these techniques effectively preserve gradient
signal strength, prevent premature convergence, and increase training stability across diverse
environment configurations. The findings highlight the importance of integrated mitigation strategies
that address both temporal and structural dimensions of RL optimization.