Author

Haoze Lv

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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

Preprint Aug 2026

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

CoPES is introduced, a cooperative coevolutionary method that decomposes the full parameter space into lower-dimensional subspaces and searches over them cooperatively to improve optimization efficiency and demonstrate an improved trade-off between memory requirements and training time for agentic LLM post-training und...

Zhiyuan Wang, Sheng-Cai Liu, Jiahao Wu et al. · 2 citations

Preprint Aug 2026

MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble

Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, existing methods primarily optimize a single heuristic, whereas practical optimization frameworks often rely on multiple interacting components...

Haoze Lv, Ning Lu, Shengcai Liu et al. · 0 citations