arXiv:2605.26352v1 Announce Type: new Abstract: Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate queries, and search again. Training such agents raises a credit-assignment challenge: executable actions such as queries or summaries can be directly evaluated by the retriever, while latent reasoning steps are not directly observable and only affect future executable actions. This asymmetry makes outcome-level reward assignment unreliable, as the same final reward may credit reasoning steps that di
The increasing complexity of AI agent interactions and the shift towards interactive reasoning necessitate advancements in how these systems learn and are rewarded.
Improving credit assignment in reasoning agents is crucial for developing more effective and autonomous AI systems, leading to better performance in complex tasks.
This research provides a novel method, RICE-PO, to overcome the credit-assignment problem in interactive AI agents, making their learning process more efficient and robust.
- · AI companies developing agentic systems
- · Researchers in reinforcement learning
- · SaaS platforms leveraging AI agents
- · Companies with less sophisticated AI agent architectures
AI agents will become more adept at complex, multi-step problem-solving through improved credit assignment.
Enhanced agent capabilities will accelerate the automation of white-collar workflows and specialized tasks.
The increased efficiency and reliability of AI agents could lead to significant restructuring of service industries and new forms of human-AI collaboration.
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Read at arXiv cs.CL