5 papers · 1 filter
Lookahead Routing for Large Language Models
Canbin Huang, Tianyuan Shi, Yuhua Zhu +2
Large language model (LLM) routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of het…
SPELL: Self-Play Reinforcement Learning for Evolving Long-Context Language Models
Ziyi Yang, Weizhou Shen, Chenliang Li +5
Progress in long-context reasoning for large language models (LLMs) has lagged behind other recent advances. This gap arises not only from the intrinsic difficulty of processing lo…
Discriminative Policy Optimization for Token-Level Reward Models
Hongzhan Chen, Tao Yang, Shiping Gao +4
Process reward models (PRMs) provide more nuanced supervision compared to outcome reward models (ORMs) for optimizing policy models, positioning them as a promising approach to enh…
Self-Evolution Fine-Tuning for Policy Optimization
Ruijun Chen, Jiehao Liang, Shiping Gao +2
The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and a…
BlockPruner: Fine-grained Pruning for Large Language Models
Longguang Zhong, Fanqi Wan, Ruijun Chen +2
With the rapid growth in the size and complexity of large language models (LLMs), the costs associated with their training and inference have escalated significantly. Research indi…