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One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness

Tajdini, Artin et al.
arXiv (All) · Papers
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artificial-intelligencecomputation-and-languagemachine-learning
Post-training binds the competence of an LLM agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the agent worse at it. Scoring each sample independently mean
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