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20212026
most citedOn The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability

6 citations · 16 across the 15 of their papers we have counts for

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cs.LG2026

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

Junbo Li, Peng Zhou, Rui Meng +3

Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Re…

cs.LG2025

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

Geon-Woo Kim, Junbo Li, Shashidhar Gandham +5

Training large language models (LLMs) increasingly relies on geographically distributed accelerators, causing prohibitive communication costs across regions and uneven utilization…

cs.LG2025

PIPA: Preference Alignment as Prior-Informed Statistical Estimation

Junbo Li, Zhangyang Wang, Qiang Liu

Offline preference alignment for language models such as Direct Preference Optimization (DPO) is favored for its effectiveness and simplicity, eliminating the need for costly reinf…

cs.LG2023

FedNAR: Federated Optimization with Normalized Annealing Regularization

Junbo Li, Ang Li, Chong Tian +3

Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prev…

cs.LG2021★ 2 cited

Exponential Family Model-Based Reinforcement Learning via Score Matching

Gene Li, Junbo Li, Anmol Kabra +3

We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family di…