arXiv:2602.00845v3 Announce Type: replace Abstract: Agentic reasoning enables large reasoning models (LRMs) to dynamically acquire external knowledge, but yet optimizing the retrieval process remains challenging due to the lack of dense, principled reward signals. In this paper, we introduce InfoReasoner, a unified framework that incentivizes effective information seeking via a synthetic semantic information gain reward. Theoretically, we redefine information gain as uncertainty reduction over the model's belief states, establishing guarantees, including non-negativity, telescoping additivity,
The rapid advancement and adoption of large reasoning models necessitate more sophisticated and efficient methods for knowledge acquisition and optimization, driving research into practical reward mechanisms.
This development offers a principled approach to overcoming a key bottleneck in autonomous AI agents, making them more effective at real-world problem solving and decision making.
The introduction of a semantic information gain reward provides a dense, theoretically grounded signal for optimizing agentic retrieval, potentially leading to significantly more efficient and capable AI agents.
- · AI Agent developers
- · Companies implementing AI agents
- · Research institutions in AI
- · Inefficient AI agent models
- · Manual knowledge engineering processes
More robust and efficient AI agents capable of dynamic knowledge acquisition.
Accelerated deployment of AI agents in complex, unstructured environments.
Increased automation of white-collar tasks by agents requiring less human oversight.
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Read at arXiv cs.AI