5 papers
Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation
Li-Rong Zhou, Qin-Wen Luo, Sheng-Jun Huang
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage. Action preference que…
NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
Shaohua Liu, Liang Fang, Yilong Sun +16
Industrial advertising recommender systems are continually improved through architecture modifications, yet production iteration remains expert-intensive because coordinated change…
Compress the Easy, Explore the Hard: Difficulty-Aware Entropy Regularization for Efficient LLM Reasoning
Qin-Wen Luo, Sheng Ren, Xiang Chen +4
Chain-of-Thought (CoT) has substantially empowered Large Language Models (LLMs) to tackle complex reasoning tasks, yet the verbose nature of explicit reasoning steps incurs prohibi…
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset. To alleviate extrapolation errors, existing studies often uniformly regularize the valu…
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline-to-online (O2O) reinforcement learning (RL) provides an effective means of leveraging an offline pre-trained policy as initialization to improve performance rapidly with li…