Anchored Bipolicy Self-Play trains role-specific LoRA adapters on a frozen base model to break self-consistency collapse in self-play red-teaming, yielding up to 100x parameter efficiency and stronger safety on Qwen2.5 models.
Chasing moving targets with online self-play reinforcement learning for safer language models
6 Pith papers cite this work. Polarity classification is still indexing.
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ClawdGo uses a self-play training loop with weakest-first scheduling and cross-session memory to raise AI agents' security awareness scores from 80.9 to 96.9 across 12 taxonomy dimensions.
Introduces structured Stackelberg games and the Stackelberg-Littlestone dimension to characterize the leader's optimal regret and sample complexity when context predicts follower type.
SEAR trains one LLM via adversarial process rewards to explore harmful reasoning paths but flip to safe outputs, reducing over-refusal while preserving safety.
Equation-to-Behavior Prompting lets large LLMs match cognitive models like Bayesian updating in persuasion games; RL training cuts small-model belief error by 26.5% and improves diverse training outcomes by 2.5-12%.
PIA achieves lower attack success rates on persona-based jailbreaks via self-play co-evolution of attacks (PLE) and defenses (PICL) that structurally decouples safety from persona context using unilateral KL-divergence.
citing papers explorer
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Poster: ClawdGo: Endogenous Security Awareness Training for Autonomous AI Agents
ClawdGo uses a self-play training loop with weakest-first scheduling and cross-session memory to raise AI agents' security awareness scores from 80.9 to 96.9 across 12 taxonomy dimensions.