2 papers
cs.AI2026
SAGE: Multi-Agent Self-Evolution for LLM Reasoning
Yulin Peng, Xinxin Zhu, Chenxing Wei +4
Reinforcement learning with verifiable rewards improves reasoning in large language models (LLMs), but many methods still rely on large human-labeled datasets. While self-play redu…
cs.SE2026
SEMAG: Self-Evolutionary Multi-Agent Code Generation
Yulin Peng, Haowen Hou, Xinxin Zhu +2
Large Language Models (LLMs) have made significant progress in handling complex programming tasks. However, current methods rely on manual model selection and fixed workflows, whic…