Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling
arXiv:2605.29697v1 Announce Type: new Abstract: In Agentic Search, trajectory-level outcome rewards fail to quantify the behavioral contributions of individual steps, while existing step-level reward methods typically rely on costly tree sampling. We view world knowledge as a latent world graph and each IS task as search within a latent task graph, where effective steps should make graph progress toward the answer node. Based on this prior, we propose Graph-Distance Contribution Reward (GDCR), a step-level process reward that scores newly-retrieved and newly-cited entities by their distance to
The proliferation of advanced AI models demands more efficient and cost-effective methods for training and fine-tuning agentic behaviors, moving beyond expensive traditional sampling methods.
Improved credit assignment mechanisms for AI agents directly enhance their effectiveness and efficiency, potentially accelerating their adoption in complex tasks and reducing computational overhead.
The proposed method (GDCR) offers a new paradigm for rewarding step-level contributions in agentic search, potentially leading to more sophisticated and autonomous AI agents with less training cost.
- · AI developers
- · Companies deploying AI agents
- · Cloud computing providers (due to increased agent efficiency)
- · Companies reliant on less efficient, trajectory-level reward systems
AI agents become more capable and cost-efficient at performing complex, multi-step search tasks.
Accelerated development and deployment of autonomous AI systems across various industries, replacing manual knowledge work.
The economic impact of AI agent deployment could reshape labor markets and drive demand for new forms of human-agent collaboration and oversight.
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Read at arXiv cs.AI