DEPT detects training impasses in social language agents via dual-scale divergence and entropy, then uses asymmetric reshaping to restore exploration gradients and prevent policy homogenization.
Player 1 would be worse off than refusing to cooperate at all ( which would result in $0 .00 for both players )
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Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
DEPT detects training impasses in social language agents via dual-scale divergence and entropy, then uses asymmetric reshaping to restore exploration gradients and prevent policy homogenization.