Multi-Turn AI Agents Need Coverage, Not Just Targeted Credit

In the evolving field of multi-turn agentic Reinforcement Learning (RL), credit assignment is often framed as a targeting problem, where methods attempt to pinpoint which specific turns contributed most to a final verifiable reward. This paper challenges that perspective, identifying a structural quantity called verifier information density (V_d) that determines the effectiveness of targeting. When V_d is low, meaning the verifier exposes only a small fraction of the agent's causal chain correctness, targeting becomes suboptimal. Through controlled experiments, the study demonstrates that a continuous, uniformly distributed dense reward consistently outperforms sparse binary outcome rewards, which can even be detrimental. This effect holds even when the same advantage is concentrated on "progress turns" or random turns, indicating that the mechanism at play is coverage of the causal chain, not precise targeting. The research establishes a synthetic phase boundary, showing that targeting only becomes effective at high V_d, whereas current benchmarks operate in a low V_d regime. The findings suggest that uniform redistribution serves as a robust default for credit assignment when information is sparse, and per-turn schemes must significantly improve coverage to surpass it.