The Consensus Paradox: When Low Disagreement Leads to Catastrophic Failure in Multi-teacher Reinforcement Learning

In multi-teacher reinforcement learning, conventional wisdom suggests that combining expert knowledge through ensemble methods should improve performance. We reveal a striking paradox: in environments with changing goals, ensemble methods that achieve the highest agreement among teachers deliver the worst performance (32.3% success rate) – even worse than random teacher selection (34.5%). Through controlled experiments in a drifting grid world where four expert teachers guide a learning agent, we demonstrate that confidence-weighted voting creates false consensus by amplifying outdated expertise. Our analysis of 30 random seeds (F = 8957.6, p < 0.0001) shows that when environments change, teacher disagreement is not noise to be reduced but a valuable signal of adaptation. We introduce the Teacher Confusion Index (TCI) and Goal Coherence Score (GCS) to quantify this phenomenon, revealing a positive correlation (r = 0.277) between disagreement and performance. These findings challenge fundamental assumptions about ensemble learning in non-stationary environments, with implications for any multi-expert system facing concept drift.