Author

Jaehyung Kim

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#machine learning Preprint Oct 2026

Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following

CALIF is a lightweight method that augments verifier rewards with a calibrated likelihood-based bonus to preserve response quality and outperforms corresponding base policies in 40 of 45 model-metric comparisons across three rubric-based instruction-following benchmarks.

Sangyeon Yoon, Wooseok Seo, Hamish Ivison et al. · 0 citations

#artificial intelligence Jun 2025

Training-free LLM Verification via Recycling Few-shot Examples

This work proposes a novel framework that Recycles Few-shot examples to verify LLM outputs (ReFeri), which combines a forward confidence score with a backward reconstruction penalty to select candidates that follow few-shot guidance while avoiding demonstration-specific overfitting.

Dongseo Lee, Jimyung Hong, Dongyoung Kim et al. · arXiv.org · 6 citations