#multi-agent-training News & Analysis

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

Researchers propose DAC (Divide and Cooperate), a multi-agent training framework that separates evidence retrieval and answer generation into two specialized agents with cross-agent learning signals. This approach addresses credit assignment problems in language models performing multi-step reasoning and achieves competitive performance using parameter-efficient LoRA modules, outperforming full fine-tuning baselines on QA benchmarks.