Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question answering, while outcome-only reinforcement learning provides credit signals that are too coarse to optimize intermediate steps. We propose \textsc{EvalAct} (Evaluate-as-Action), which converts implicit retrieval quality assessment into an explicit action and enforces a coupled Search-to-Evaluate protocol so that each retrieval is immediately followed by a structured evaluation score, yielding process signals aligned with the interaction trajectory. To leverage these signals, we introduce Process-Calibrated Advantage Rescaling...
| Authors | Jiangming Shu, Yuxiang Zhang, Ye Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 March 2026 |
| Last updated at source | 12 March 2026 |
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