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Reinforcement learning with a verifiable reward (RLVR) offers a scalable approach to training language-model agents, yet sparse outcome rewards can leave early training with little signal for policy improvement. We identify an On-Policy Acceleration Phenomenon: in our main comparisons, RLVR initialized with on-policy d...
In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements referenced in a clinical query. Even when database retrieval succeeds, clinical agents can overlook such discrepancies and return plausible but unsupported answers. We intro...
DeShortcut-Align is proposed, a shortcut-decoupling alignment framework that reduces dependence on superficial cues that significantly improves robustness against template-stripping bypass attacks, substantially reduces over-refusal, and better preserves general-purpose reasoning capabilities, thereby mitigating the al...
Efficient On-Policy Self-Distilled Safety Alignment (EOPSA), which concentrates computational and gradient budgets exclusively on reliably supervised, safety-critical tokens, is proposed, consistently outperforming full-token distillation baselines in both safety compliance and reasoning retention.
SALA is proposed, a Semantic-Aware Logical Alignment framework that automatically learns task-specific reasoning operations and uses dynamic time warping to align the reasoning sequences.
A conflict-driven preference optimization framework for model merging (CoMerge), which reformulates model merging as a preference optimization problem and utilizes a self-supervised, conflict-driven strategy that leverages the defects of naive merging methods as hard negative samples to construct preference pairs witho...
This work introduces AgentSnare, a trajectory-adaptive deception system that dynamically unfolds a decoy environment to continually steer the penetration agent away from the real target.
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