迁移学习旨在减少深度强化学习（RL）的高样本复杂性，但现有方法在源环境和目标环境之间域偏移时表现不佳。策略蒸馏提供了强大的战术指导，但无法转移长期的战略知识，而基于自动机的方法捕捉了任务结构，但缺乏精细的动作指导。本文介绍了Context-Aware Distillation with Experience-gated Transfer（CADENT），一种将战略自动机知识与战术策略层面知识统一到一致指导信号的框架。CADENT的关键创新是一个经验门控信任机制，能够在状态-动作层面动态权衡教师指导与学生自身经验，从而适应目标领域特定需求。在具有挑战性的环境中，从稀疏奖励网格世界到连续控制任务，CADENT在样本效率上比基线方法提高40-60%，同时保持优越的渐进行为性能，建立了一种稳健的适应性知识迁移方法。
英文摘要：
Transfer learning promises to reduce the high sample complexity of deep reinforcement learning (RL), yet existing methods struggle with domain shift between source and target environments. Policy distillation provides powerful tactical guidance but fails to transfer long-term strategic knowledge, while automaton-based methods capture task structure but lack fine-grained action guidance. This paper introduces Context-Aware Distillation with Experience-gated Transfer (CADENT), a framework that unifies strategic automaton-based knowledge with tactical policy-level knowledge into a coherent guidance signal. CADENT's key innovation is an experience-gated trust mechanism that dynamically weighs teacher guidance against the student's own experience at the state-action level, enabling graceful adaptation to target domain specifics. Across challenging environments, from sparse-reward grid worlds to continuous control tasks, CADENT achieves 40-60\% better sample efficiency than baselines while maintaining superior asymptotic performance, establishing a robust approach for adaptive knowledge transfer in RL.
Decoupling KL and Trajectories: A Unified Perspective for SFT, DAgger, Offline RL, and OPD in LLM Distillation
Eastern Institute of Technology（东技术院）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（人工智能 thrust，香港科学与技术大学（广州））
Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation
School of Computer Science and Technology, China University of Mining and Technology（计算机科学与技术学院，中国矿业大学）；Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology（教育部矿山数字化工程研究中心，中国矿业大学）
Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
School of Information and Communication Engineering, BUPT（北京邮电大学信息与通信工程学院）；BUPT: Beijing University of Posts and Telecommunications（北京邮电大学）；Engineering Research Center for Information Network, Ministry of Education（教育部信息网络工程研究中心）
We explore the use of knowledge distillation (KD) for learning compact and accurate models that enable classification of animal behavior from accelerometry data on wearable devices. To this end, we take a deep and complex convolutional neural network, known as residual neural network (ResNet), as the teacher model. ResNet is specifically designed for multivariate time-series classification. We use ResNet to distill the knowledge of animal behavior classification datasets into soft labels, which consist of the predicted pseudo-probabilities of every class for each datapoint. We then use the sof…
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
Université d’Orléans（奥尔良大学）；INSA CVL（中央卢瓦尔河谷国立应用科学学院）；CY Cergy Paris University（塞尔吉-蓬图瓦兹大学）；ENSEA（法国高等电子与数字学院）；CNRS（法国国家科学研究中心）；Université de Technologie de Troyes（特鲁瓦技术大学）
