Long-horizon autonomous research tasks such as machine learning engineering require systems to make interdependent decisions under a limited budget. Existing LLM-based agents typically organize candidate-solution improvement through tree, graph, or chain structures, meaning that the search process determines how inform...
Shaokang Fu, Yulong Tao, Linbo Jin et al.· 1 citation
SKILL-KD is proposed, a contrastive skill distillation framework that treats skills as an explicit distillation medium between agents of different capabilities and consistently improves frozen student agents over fixed-model adaptation baselines.
Qi-Ming Shi, Yibo Dou, J. Zhu et al.· arXiv.org· 0 citations
Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria. Real-world deployments often require Long-Term Coherence, the capacity to preserve purposeful behavior across extended horizons while adapting decisions to accumul...
Qi-Ming Shi, Yulong Tao, Linbo Jin et al.· arXiv.org· 2 citations
Requiring only outcome rewards and no process-level annotation, CBPO provides a practical solution for fine-grained credit assignment in tool-integrated agent training and consistently outperforms state-of-the-art policy-optimization and branch-based methods.
Ying Wang, Changlin Qiu, Bang Lin et al.· 0 citations
This work proposes BCSD (Bidirectional Context Self-Distillation), a framework that combines self-distillation with reinforcement learning to train LLM agents to use external skills more effectively, enabling agents to utilize external skills more effectively.
Tian Pan, Yuan Li, Hong-Da Wang et al.· 1 citation
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