Oct 24, 2026 · Artificial Intelligence

VAST: Adaptive Horizon Selection for Long-Horizon RL via Value Stitching

Researchers from HKU and Tsinghua propose VAST, an offline RL framework that adaptively chooses time horizons for value updates by stitching variable-length trajectory segments, improving long-horizon decision-making across 50 OGBench tasks.

Adaptive Horizon SelectionLong-Horizon RLNeurIPS 2026

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VAST: Adaptive Horizon Selection for Long-Horizon RL via Value Stitching

Data Party THU

Sep 9, 2026 · Artificial Intelligence

Online and Off-Policy Learning for Large Action Spaces: Structured Exploration & Optimization

This PhD thesis addresses large action space contextual bandits, proposing mixed-effects and diffusion Thompson sampling for online learning, and structured direct methods, policy-weighted likelihood, exponential smoothing, and PAC-Bayes pessimism for offline learning, showing that action structure, optimizable objectives, and pessimism are crucial for scalable decision-making.

Offline Policy LearningPAC-BayesThompson sampling

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Online and Off-Policy Learning for Large Action Spaces: Structured Exploration & Optimization