Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
Beyond Quantity: Trajectory Diversity Scaling for Code Agents
Guhong Chen, Chenghao Sun, Cheng Fu +16
As code large language models (LLMs) evolve into tool-interactive agents via the Model Context Protocol (MCP), their generalization is increasingly limited by low-quality synthetic…
cs.AI2024
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
Feiteng Fang, Yuelin Bai, Shiwen Ni +3
Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-…
cs.AI2024
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Feiteng Fang, Liang Zhu, Min Yang +6
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial a…