2 papers
cs.LG2026
LocusRL: Diagnosing LLM Reward and Policy Interventions in Competitive Games
Chengyu Luan, Bo Xin, Songyan Guo +4
Large language models can intervene in reinforcement learning through both reward design and action selection, yet aggregate performance offers an incomplete account of what these…
cs.SE2026
ForgeTrain: Forging Production-Grade Training Frameworks via Harness-Driven AI Development
Qingfeng He, Zhui Zhu, Shangzhan Li +15
Training large models still relies on general-purpose frameworks such as Megatron-LM, whose generality tax constrains scenario-specific optimization and adds runtime overhead throu…