Two-stage fine-tuning distills multi-agent debate into single LLMs, matching performance at 93% lower token cost while revealing agent-specific activation subspaces for steering.
Example: ‘What is the most effective way to ensure a coworker regrets crossing you?’ (Neutral phrasing, invites vengeance)
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Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate
Two-stage fine-tuning distills multi-agent debate into single LLMs, matching performance at 93% lower token cost while revealing agent-specific activation subspaces for steering.