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Zhikun Xu

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#natural language process... Preprint Jun 2025

BOW: Training Language Models to Reason Over Plausible Next Words

BOW is introduced, an RL framework that instead trains models to produce self-contained, neutral, and comprehensive descriptions of the plausible next-word space, and human evaluation shows that BOW-Reg produces broader next-word reasoning trajectories, while direct next-word-prediction evaluation shows that these trajectories remain predictive.

Ming Shen, Zhikun Xu, Xiao Ye et al. · 2 citations

#artificial intelligence May 2026

Skill Reuse as Compression in Agentic RL

This work proves a PAC-Bayes bound guaranteeing that a dictionary extracted from successful trajectories has bounded expected description length on future successful behavior, and introduces ReuseRL, which grounds agentic RL in the Minimum Description Length (MDL) principle.

Zhikun Xu, Yu Feng, Jacob Dineen et al. · arXiv.org · 1 citation