5 papers
Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression
Junqi Gao, Dazhi Zhang, Zhichang Guo +3
Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Among existing approaches, dyna…
MARS: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation
Pengfei Li, Shijie Wang, Fangyuan Li +7
Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited trajectory diversity often leads t…
DARE: Diffusion Large Language Models Alignment and Reinforcement Executor
Jingyi Yang, Yuxian Jiang, Xuhao Hu +3
Diffusion large language models (dLLMs) are emerging as a compelling alternative to dominant autoregressive models, replacing strictly sequential token generation with iterative de…
WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement
Fangyuan Li, Pengfei Li, Shijie Wang +4
Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing methods face a key trade-off:…
PDAC: Efficient Coreset Selection for Continual Learning via Probability Density Awareness
Junqi Gao, Zhichang Guo, Dazhi Zhang +3
Rehearsal-based Continual Learning (CL) maintains a limited memory buffer to store replay samples for knowledge retention, making these approaches heavily reliant on the quality of…