2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
Yuheng Zhang, Dian Yu, Tao Ge +5
Reinforcement learning from human feedback (RLHF) has demonstrated remarkable effectiveness in aligning large language models (LLMs) with human preferences. Many existing alignment…
cs.CL2025★ 2 cited
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
Xingyu Chen, Jiahao Xu, Tian Liang +11
The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended c…
cs.CL2024
HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows
Wenlin Yao, Haitao Mi, Dong Yu
Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still lim…