Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation
We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a fixed reward threshold, our method formulates adversarial data curation as a failure-mode contextual bandit problem. Candidate examples are generated with retrieval-augmented prompting, filtered by the current target model, automatically validated by an LLM judge ensemble, and clustered into recurring failure modes. A stochastic policy then selects which failure modes to sample for retraining, and is updated using validation-based reward that balances robustness gains, forgetting, and data cost. This makes the da...
| Authors | Roie Kazoom, Ofir Cohen, Rami Puzis et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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