52 citations · 155 across the 26 of their papers we have counts for
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cs.AI2026
EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents
Min Cai, Dan Zhang, Gao Xing +3
Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on the user simulator and environm…
cs.AI2024★ 1 cited
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye +4
Instruction Fine-Tuning (IFT) significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). While coding data is known to boost LLM reasoning abilit…