Author

Daniel Kang

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Preprint Jul 2026

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards

This work establishes the first non-vacuous generalization bounds for parameter-efficient RLVR fine-tuning at the billion-parameter scale, and proposes the Progressive RLVR framework, which integrates RLVR with on-policy distillation, TinyLoRA, and model quantization.

Yuxuan Zhu, Rohan Alur, Daniel Kang · 0 citations