Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention. However, fixed outcome-based rewards do not distinguish incorrect responses with different confidence levels or account for...
Qianchi Zhang, Hainan Zhang, Liang Pang et al.· 0 citations
Domain-specific continual adaptation of LLMs risks catastrophic forgetting, creating a fundamental tension between acquiring new capabilities and preserving those learned during pretraining. PEFT mitigates this problem by restricting the number of trainable parameters, but existing methods lack a principled unit for de...
Ling-Xiang Wang, Hai-Nan Zhang, Liang Pang et al.· 0 citations
In social dilemmas, individuals need to forgo short-term temptations to achieve synergistic collective outcomes through cooperation. Previous work has examined mechanisms through which cooperation can evolve, including direct reciprocity, indirect reciprocity, environmental stochasticity, network reciprocity, and demog...
Yu-Xin Geng, Xing-Ru Chen, Xin Wang et al.· 0 citations
In social dilemmas, individuals need to forgo short-term temptations to achieve synergistic collective outcomes through cooperation. Previous work has examined mechanisms through which cooperation can evolve, including direct reciprocity, indirect reciprocity, environmental stochasticity, network reciprocity, and demog...
Yu-Xin Geng, Xing-Ru Chen, Xin Wang et al.· 0 citations
Reinforcement learning provides a framework for studying how individuals adjust their behavior through repeated interaction and feedback in social dilemmas. In Q-learning, exploration controls how often agents choose actions other than those favored by their current learned Q-values. Yet, the existing models usually tr...
Ang Li, Wenqiang Zhu, Chao-Qian Wang et al.· Chaos· 0 citations
This work shows that informative embeddings can be derived without complicated model design and gradient-based training, and suggests that informative graph embeddings can arise from carefully chosen topological transformations before any learning operation is applied.
Meng Qin, Jin-Qiang Cui, Hong-Wei Zheng et al.· 0 citations
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