KbSD: Knowledge Boundary aware Self-Distillation for Behavioral Calibration in Agentic Search
Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memory, when to rely on retrieved evidence, and when to abstain. Binary rewards can penalize undesirable outcomes, but provide little guidance on the reasoning process required to make calibrated decisions across different knowledge states. To address this, we propose KbSD (Knowledge boundary Self-Distillation), a framework that tackles this limitation through dense token-level supervision, outcome-level sparse rewards, and quadrant-adaptive optimization. KbSD const...
| Authors | Tao Feng, Xinke Jiang, Chao Wu |
|---|---|
| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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