3 papers
cs.AI2026
Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
Tianle Wang, Zhaoyang Wang, Guangchen Lan +4
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered…
cs.LG2026
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang +7
As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving perfor…
cs.LG2025
Anyprefer: An Agentic Framework for Preference Data Synthesis
Yiyang Zhou, Zhaoyang Wang, Tianle Wang +13
High-quality preference data is essential for aligning foundation models with human values through preference learning. However, manual annotation of such data is often time-consum…