Author

Rui-Nan Jin

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#artificial intelligence Preprint Oct 2026

Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis

Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earlier policies. Theoretical understanding of how this staleness affects convergence and how to mitigate its impact remains limited. We derive a convergence bound for GRPO-s...

Qi-Jia He, Rui-Nan Jin, Jun Luo et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Adam under Generalized Smoothness with Second-Moment-Type Stochastic Gradients

Adam is widely observed to remain stable even when the objective deviates significantly from global smoothness. Under the generalized smoothness framework, however, existing analyses rely on strong tail assumptions on the stochastic gradients, such as almost-sure boundedness or sub-Gaussianity. Whether Adam converges o...

Rui-Nan Jin, Di-Fei Cheng, Ling Chen et al. · 0 citations