4 papers
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models
Tingyun Li, Zishang Jiang, Jinyi Han +8
Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-orient…
SEIF: Self-Evolving Reinforcement Learning for Instruction Following
Qingyu Ren, Qianyu He, Jiajie Zhu +7
Instruction following is a fundamental capability of large language models (LLMs), yet continuously improving this capability remains challenging. Existing methods typically rely e…
LsrIF: Enhancing Logic-Structured Instruction Following of Large Language Models
Qingyu Ren, Qianyu He, Jingwen Chang +9
Instruction following is critical for large language models, yet real-world instructions often involve multiple constraints with logical structures, such as parallel composition, s…
Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data
Han Xia, Songyang Gao, Qiming Ge +3
Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Pr…