Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents

• URL: http://arxiv.org/abs/2603.09203v2
• Date: Thu, 12 Mar 2026 06:04:58 GMT
• Title: Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
• Abstract summary: textscEvalAct (Evaluate-as-Action) converts implicit retrieval quality assessment into an explicit action.<n>textscEvalAct achieves the best average accuracy, with the largest gains on multi-hop tasks.
• Score: 24.080021799509847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (PCAR), a GRPO-based optimization method that rescales advantages at the segment level according to evaluation scores, emphasizing reliable segments while updating uncertain ones conservatively. Experiments on seven open-domain QA benchmarks show that \textsc{EvalAct} achieves the best average accuracy, with the largest gains on multi-hop tasks, and ablations verify that the explicit evaluation loop drives the primary improvements while PCAR provides consistent additional benefits.

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