Steering Equilibrium Selection in Regularized Self-Play via the Reference Policy

A theoretical paper on controlling which equilibrium self-play agents converge to, with implications for steering AI training dynamics.

Auto-scored: Technical equilibrium-selection result with only speculative dual-use framing and no demonstrated misuse or capability jump.