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Author

Dong Yu

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Conference Open access 2026

Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains

This work provides a scalable and effective framework for extending RLVR beyond the limitations of pattern-based verification to complex, noisy, real-world domains, and generalizes strongly to seven out-of-distribution benchmarks.

Yi Su, Dian Yu, Linfeng Song et al. · 1 citation
Conference Open access 2026

Your Reasoning Model is Secretly a Reward Model - Optimization-Free Verification from Experience

This paper introduces C LUE (Clustering and Experience-based Verification) , a training-free, non-parametric verifier that improves selection and reranking in Large Language Model outputs and finds that correct and incorrect solutions exhibit measurable geometric differences in their hidden-state trajectories.

Zhenwen Liang, Ruosen Li, Yujun Zhou et al. · 0 citations