Process Reward Agents for Steering Knowledge-Intensive Reasoning
Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources. As a result, subtle errors can propagate through reasoning traces, potentially never to be detected. Prior work has proposed process reward models (PRMs), including retrieval-augmented variants, but these methods operate post hoc, scoring completed trajectories, which prevents their integration into dynamic inference procedures. Here, we introduce Process Reward Agents (PRA), an inference-time method for providing domain-grounded, online, step-wise reward...
| Authors | Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 1 June 2026 |
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