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Marginal-Contribution Policy Gradients under Filtered Feedback for Multi-Agent LLMs
We develop a unified treatment of credit assignment for RL training in multi-agent LLM systems. We show that observed reward alone cannot distinguish an agent that determines it from one that never affects it, and that standard shared-reward training performs exact gradient ascent on each agent's pr
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