2 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
The Optimal Token Baseline: Variance Reduction for Long-Horizon LLM-RL
Yingru Li, Jiawei Xu, Ziniu Li +10
Reinforcement Learning (RL) for Large Language Models (LLMs) often suffers from training collapse in long-horizon tasks due to exploding gradient variance. To mitigate this, a base…
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning
Zhenghai Xue, Longtao Zheng, Qian Liu +4
Large Language Models (LLMs) can significantly improve their reasoning capabilities by interacting with external tools, a paradigm known as Tool-Integrated Reasoning (TIR). However…
Group-in-Group Policy Optimization for LLM Agent Training
Lang Feng, Zhenghai Xue, Tingcong Liu +1
Recent advances in group-based reinforcement learning (RL) have driven frontier large language models (LLMs) in single-turn tasks like mathematical reasoning. However, their scalab…
Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning
Zhenghai Xue, Lang Feng, Jiacheng Xu +4
To learn from data collected in diverse dynamics, Imitation from Observation (IfO) methods leverage expert state trajectories based on the premise that recovering expert state dist…
SAC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
Safa Messaoud, Billel Mokeddem, Zhenghai Xue +4
Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy…