4 papers
LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards
Bowen Ping, Zijun Chen, Yiyao Yu +3
Reinforcement Learning has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios--such as long-dialogue understanding and structur…
DecIF: Improving Instruction-Following through Meta-Decomposition
Tingfeng Hui, Pengyu Zhu, Bowen Ping +4
Instruction-following has emerged as a crucial capability for large language models (LLMs). However, existing approaches often rely on pre-existing documents or external resources…
Smaller Language Models Are Better Instruction Evolvers
Tingfeng Hui, Lulu Zhao, Guanting Dong +3
Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they…
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang +3
Mixture-of-Experts (MoE) shines brightly in large language models (LLMs) and demonstrates outstanding performance in plentiful natural language processing tasks. However, existing…