Author

Zi-Qiang Dong

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#machine learning Preprint Oct 2026

P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing

This work introduces Page-to-Token Policy Optimization (P2TPO), a structure-aligned credit-assignment framework that operates on full-page policy trajectories that outperforms all evaluated policy optimization baselines in seed-averaged scores on each of the six document parsing benchmarks.

Yong-Zhou-Shi-、-Lan-Shan-Xian-Ti-Gai-Wei-Lian-He-D Zu, Ning-Hua Yang, Ze-Zhong Tan et al. · 0 citations

#artificial intelligence May 2026

Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective

ConSPO is a Contrastive Sequence-level Policy Optimization method that uses length-normalized sequence log-probabilities as rollout scores and contrasts verified positive rollouts against negative distractors within the same group, and outperforms strong baselines on challenging reasoning benchmarks.

Feng Zhang, Xin-Hong Ma, Zi-Qiang Dong et al. · arXiv.org · 1 citation

Preprint Aug 2026

I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization

I-SDPO (Instance-Level Adaptive Self-Distillation Policy Optimization), which treats teacher reliance as capability-dependent and uses imitation only where group-relative rewards are uninformative, obtains the best result in all four scientific domains.

Yubo Zhang, Xin-Hong Ma, Zezhong Tan et al. · 1 citation