Search-G1: Training Grounded Search Agents via Representation-Based Intrinsic Rewards
_reachsumit · x · 2026-08-11
Existing search-augmented language agents face a reward dilemma: outcome rewards are sparse, while richer process feedback requires costly annotations. Internal rewards (like entropy) are cheap but mainly reflect model confidence rather than evidence grounding.
To address this, the paper proposes Search-G1, a representation-based intrinsic reward framework. It measures the operational grounding of an agent's answers through two intervention-calibrated readouts:
- Prompt-state readout: Predicts closed-book sufficiency to define policy-relative retrieval necessity.
- Answer-commit readout: Estimates evidence reliance by measuring answer-stage sensitivity to evidence deletion.
This mechanism provides extra credit to correct searched trajectories when retrieval is necessary and evidence-sensitive, favors direct answers when closed-book knowledge suffices, and penalizes redundant search. This approach effectively improves the grounding and efficiency of search agents.
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