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Yu-Xiang Yan

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Open access Aug 2026

A Center-Guided Reinforcement Learning Method for Hyperparameter Optimization and Its Application to Relation Extraction

Hyperparameter optimization (HPO) aims to identify high-quality model configurations under a limited evaluation budget. To address mixed search spaces, sparse feedback, and low sample efficiency in reinforcement learning (RL)-based HPO, a Center-Guided Reinforcement Learning (CGRL) method is proposed. In CGRL, the policy output is reformulated from a configuration to be directly evaluated into a search center that defines a promising region, decoupling region-level guidance from exact configuration selection. A mixed candidate pool is generated around the center, and a promising candidate for real evaluation is selected by a Random Forest surrogate model. Meanwhile, a process-aware reward provides dense and informative feedback for policy learning. Experiments on 20 Yet Another Hyperparameter Optimization (YAHPO) Gym environments validate the effectiveness of CGRL. Compared with random search (RS), Tree-structured Parzen Estimator (TPE), Sequential Model-based Algorithm Configuration 3 (SMAC3), a Proximal Policy Optimization baseline (PPO-basic), Hyperparameter Optimization by Reinforcement Learning (Hyp-RL), and Q-Learning for Hyperparameter Optimization (HyperQ-Opt), CGRL achieves the best average rank of 1.800 in terms of the final best objective value, versus 6.000, 3.600, 2.200, 4.450, 6.350, and 3.600, respectively. For Low-Rank Adaptation (LoRA) HPO for relation extraction (RE) from ancient Chinese historical documents, CGRL improves Macro-F1 by 8.66%, 3.10%, 3.18%, and 5.13% on the validation set relative to RS, TPE, SMAC3, and PPO, respectively, and by 11.15%, 2.03%, 3.11%, and 9.59% on the test set. These results demonstrate the effectiveness of CGRL for limited-budget HPO and its applicability to practical RE tasks.

Y. Tan, Li-Ping Mo, Yu-Xiang Yan · Machine Learning and Knowled... · 0 citations

Preprint Aug 2026

CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning

Human-in-the-loop real-world reinforcement learning enables rapid acquisition of effective robotic manipulation policies for individual tasks, often within tens of minutes. Yet it remains unclear how to extend this paradigm to continual learning, where a single policy must acquire new skills without losing previously learned behaviors. Existing real-world continual learning methods do not explicitly constrain prior behaviors, leading to severe catastrophic forgetting. We introduce Continual Interactive Distillation for Embodied Reinforcement Learning (CIDER), a continual reinforcement learning framework that freezes the accumulated historical policy as a teacher before learning each new task and interleaves task learning with distillation-based retention. We further introduce gradient routing to separate the gradients used for acquiring new tasks from those used for preserving prior behaviors. We evaluate our method with a single shared actor on six real-world household and industrial manipulation tasks. Interactive Distillation maintains high measured success on previously learned tasks across our six-task real-robot sequence while acquiring each new task in 10 to 20 minutes, whereas every baseline forgets at least one previous task. Additional ablations reveal the key design choices that govern the tradeoff between stability and plasticity in real-world continual reinforcement learning.

Houlin Li, Ming Xu, Guofeng Xu et al. · 0 citations