5 papers
From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning
Yichen Lin, Xuyuan Xiong, Xue Wang +3
Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task inference from context, but makes…
LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
Yao Lai, Xuyuan Xiong, Zeyue Xue +7
In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the wavelength of light, optical…
SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
Xuyuan Xiong, Pedro Chumpitaz-Flores, Kaixun Hua +1
Interpretable reinforcement learning policies are essential for high-stakes decision-making, yet optimizing decision tree policies in Markov Decision Processes (MDPs) remains chall…
Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders
Siyu Chen, Heejune Sheen, Xuyuan Xiong +2
We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training…
HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
Simeng Han, Tianyu Liu, Chuhan Li +2
LLMs approach logical and mathematical reasoning through natural or symbolic languages. While natural language offers human-accessible flexibility but suffers from ambiguity, symbo…