4 papers
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval
Xin Xie, Dongyun Xue, Wuguannan Yao +5
LLM-powered systems require complex multi-step decision-making abilities to solve real-world tasks, yet current planning approaches face a trade-off between the high latency of inf…
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation
Jianfeng Cai, Jinhua Zhu, Ruopei Sun +5
The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provid…
Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks
Ruopei Sun, Jianfeng Cai, Jinhua Zhu +5
RLHF has emerged as a predominant approach for aligning artificial intelligence systems with human preferences, demonstrating exceptional and measurable efficacy in instruction fol…
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
Kangwen Zhao, Jianfeng Cai, Jinhua Zhu +5
Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wh…