Author

Baijiong Lin

2 papers indexed here

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#artificial intelligence Jun 2026

Policy and World Modeling Co-Training for Language Agents

PaW is a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm, and shows consistent improvements over strong RL baselines across models and RL algorithms.

Ning Lu, Baijiong Lin, Sheng-Cai Liu et al. · arXiv.org · 12 citations · ⚡2

#artificial intelligence Preprint Mar 2026

Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

This work proposes HIVE (History-Informed and online-VErified prompt selection), a dual-stage framework for data-efficient RL that utilizes historical reward trajectories for coarse selection and employs prompt entropy as a real-time proxy to prune instances with stale utility.

Jiahao Wu, Ning Lu, Sheng-Cai Liu et al. · 12 citations