Cross-Agent Learning Signals Enable Coordinated Role-Decomposed LLM Training

The paper introduces DAC (Divide and Cooperate), a role‑decomposed training framework that separates evidence acquisition (searcher) and response generation (generator) in agentic search systems. DAC uses task‑specific external verification signals to provide cross‑verification rewards, allowing the generator to abstain when evidence is insufficient and to assign credit appropriately to each role. Experiments on seven QA benchmarks and two model backbones show that DAC consistently outperforms single‑agent and multi‑agent baselines, with gains attributed to coordinated improvements in both search and generation.

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• llms
• agents
• benchmarks