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.
- Accepting the current proposal would result in Player 1 receiving $0 .01 , which is far below their required $1 .60
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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.