1 citations · 2 across the 5 of their papers we have counts for
6 papers
A Step Back: Prefix Importance Ratio Stabilizes Policy Optimization
Shiye Lei, Zhihao Cheng, Dacheng Tao
Reinforcement learning (RL) post-training has increasingly demonstrated strong ability to elicit reasoning behaviors in large language models (LLMs). For training efficiency, rollo…
Offline Behavioral Data Selection
Shiye Lei, Zhihao Cheng, Dacheng Tao
Behavioral cloning is a widely adopted approach for offline policy learning from expert demonstrations. However, the large scale of offline behavioral datasets often results in com…
State Diversity Matters in Offline Behavior Distillation
Shiye Lei, Zhihao Cheng, Dacheng Tao
Offline Behavior Distillation (OBD), which condenses massive offline RL data into a compact synthetic behavioral dataset, offers a promising approach for efficient policy training…
Revisiting LLM Reasoning via Information Bottleneck
Shiye Lei, Zhihao Cheng, Kai Jia +1
Large language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR). By leveraging s…
Off-policy Imitation Learning from Visual Inputs
Zhihao Cheng, Li Shen, Dacheng Tao
Recently, various successful applications utilizing expert states in imitation learning (IL) have been witnessed. However, another IL setting -- IL from visual inputs (ILfVI), whic…
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration
Zhihao Cheng, Liu Liu, Aishan Liu +3
Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expe…