(Andrew) Zhanke Zhou — accepted AI-conference papers

2026

Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning

(Andrew) Zhanke Zhou, Xiangyu Lu, Chentao Cao, Brando Miranda, Tongliang Liu, Bo Han +1

ICML 2026poster

RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often uses uniform sampling and near-uniform weighting, leading to inefficient computation allocation. We study GRPO by tracking token log-probabilities, group-normalized advantages, and induced toke…

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2026

Deliberate Evolution for Sample-Efficient Symbolic Regression with LLM

Xinyu Pang, (Andrew) Zhanke Zhou, Xuan Li, Fangrui Lv, Shanshan Wei, Sen Cui +2

ICML 2026poster

Symbolic regression (SR) stands as a cornerstone of scientific discovery, deriving mathematical expressions from observing data. Recent advances incorporate large language models (LLMs) into evolutionary optimization, typically relying on iterative refinement driven by scalar feedback (e.g., mean sq…

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