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
The deterministic guarantee matches the known fixed-node lower bound, and the matching randomized lower bound are proved, ensuring that both guarantees are optimal on every nondegenerate rooted tree.
Tian-Han Lu, Run-Tian Ren, Sheng-Cai Liu et al.· 3 citations
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
This work uses spatial blindness---announced requests whose locations are revealed only when visited---as a stress test: dyadic exploration preserves a constant ratio on a known finite line, while a single hidden request on a star forces a loss linear in its degree.
Tian-Han Lu, Run-Tian Ren, Sheng-Cai Liu et al.· 0 citations
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
OptiDSL is proposed, a framework that shifts the focus from rigid MILP formulations to domain-specific language (DSL) representations, and enables seamless integration with a diverse library of specialized solvers, ranging from traditional heuristics to modern learning-based methods.
Shao-Feng Zhang, Hongyuan Su, Qing Peng et al.· 0 citations
The potential gain metric is proposed, a novel metric that eliminates the need for reference solutions and consistently outperforms state-of-the-art LLM-ACP baselines, notably achieving a 19.76% relative improvement for TSP Greedy Constructive portfolios.
Shao-Feng Zhang, Sheng-Cai Liu, Zhi-Yuan Wang et al.· 0 citations
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
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