On-policy post-training can improve large language models by learning from their own trajectories, but requires costly rollout generation. We ask whether fixed demonstrations can support competitive off-policy learning through better supervision. Our premise is that their usefulness depends not only on the training tra...
Tian-Le Wang, Jia-Yu Liu, Rui-Zhi Zhao et al.· 0 citations
LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended functions. Existing eva...
Jia-Teng Liu, Ru-Shi Wang, Cheng Qian et al.· 0 citations
This work introduces a multi-agent framework designed to perform problem evolution while validating the solvability and increased difficulty of the generated problems, and provides empirical evidence that code-driven agents can serve as a viable mechanism for synthesizing high-difficulty mathematical reasoning problems...
Dadi Guo, Yue-Jin Xie, Qing-Yu Liu et al.· 0 citations
PRISK is proposed, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses.
Yumeng Wang, Yu-Chen Wu, Cheng Qian et al.· 0 citations
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