Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate
Multi-agent debate has been shown to improve reasoning in large language models (LLMs). However, it is compute-intensive, requiring generation of long transcripts before answering questions. To address this inefficiency, we develop a framework that distills multi-agent debate into a single LLM through a two-stage fine-tuning pipeline combining debate structure learning with internalization via dynamic reward scheduling and length clipping. Across multiple models and benchmarks, our internalized models match or exceed explicit multi-agent debate performance using up to 93% fewer tokens. We then investigate the mechanistic basis of this capability through activation steering, finding that i...
| Authors | John Seon Keun Yi, Aaron Mueller, Dokyun Lee |
|---|---|
| Primary category | cs.AI |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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