Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounter change. Yet current agents struggle to maintain such memory reliably. Our analysis traces this limitation to four key deficiencies: weak fine-grained visual memory, un...
Li-Zhou Liang, Xin-Yu Zhong, Miao Pan et al.· 0 citations
It is found that early OPD training dynamics uniformly exhibit a regular *useful-transfer* regime, in which held-out accuracy (the *gold score*, $G$) rises approximately linearly in $d=\sqrt{\mathrm{KL}(\pi_\theta \Vert \pi_{\mathrm{ref}})}$, the square root of token-level reverse KL divergence from the student initial...
Yun-Tai Bao, Qin-Feng Li, Guo-qing Jiang et al.· 0 citations
AttriMem is proposed, an attribution-guided process-feedback framework for learning memory-construction policies with RL that outperforms retrieval-based, heuristic, and RL-based baselines, generalizes across benchmarks and answer models, and stabilizes RL optimization.
Large language models (LLMs) have shown strong abilities in writing and revising programs, yet many program-synthesis benchmarks still evaluate programs in symbolic or digital environments. We introduce compositional machine design, a physically grounded form of program synthesis where machines are written as programs...
Wenqiang Zhang, Yang-Yi Huang, Wei-yu Liu et al.· 2 citations· ⚡1
TASPO, which converts privileged supervision into outcome-grounded action credit, is introduced and indicates that TASPO reduces supervision mismatch and that action-level assignment stabilizes the policy optimization process.
Jing-Xiao Yang, Wang-Jie Gan, Ying-Xuan Zhuang et al.· 4 citations
Experiments show that AttriMem outperforms retrieval-based, heuristic, and RL-based baselines, generalizes across benchmarks and answer models, stabilizes RL optimization, and outperforms retrieval-based, heuristic, and RL-based baselines on long-horizon dialogue question answering.
Qin-Feng Li, Yun-Tai Bao, Xinyang Yu et al.· 0 citations
SkillAligner is proposed, a training-free execution-time skill adaptation framework that treats retrieved skills as adaptable drafts rather than fixed instructions that substantially improves task performance over existing skill-use baselines, reduces skill-induced regressions at the instance level, and lowers total in...
Qin-Feng Li, Dalin He, Yun-Tai Bao et al.· 0 citations
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