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cs.CL2026
RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents
Mengyi Deng, Xin Li, Duyi Pan +4
Language agents increasingly rely on reusable skills, but post-failure repair is often handled by opaque one-shot reflection: a model generates a skill patch without explicitly mai…
cs.CL2026
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
Minrui Xu, Zilin Wang, Mengyi DENG +12
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and…
cs.CL2026
DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization
Mengyi Deng, Zhiwei Li, Xin Li +4
Although Large Language Models (LLMs) have made remarkable progress, current preference optimization methods still struggle to align directional consistency while preserving reason…