CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning

Mahyar Alinejad, Yue Wang, George K. Atia
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:4051-4059, 2026.

Abstract

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. We introduce 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-alinejad26a, title = { CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning }, author = {Alinejad, Mahyar and Wang, Yue and Atia, George K.}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {4051--4059}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/alinejad26a/alinejad26a.pdf}, url = {https://proceedings.mlr.press/v300/alinejad26a.html}, abstract = { 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. We introduce 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. } }
Endnote
%0 Conference Paper %T CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning %A Mahyar Alinejad %A Yue Wang %A George K. Atia %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-alinejad26a %I PMLR %P 4051--4059 %U https://proceedings.mlr.press/v300/alinejad26a.html %V 300 %X 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. We introduce 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.
APA
Alinejad, M., Wang, Y. & Atia, G.K.. (2026). CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:4051-4059 Available from https://proceedings.mlr.press/v300/alinejad26a.html.

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