arXiv AI By Nan Qiao, Yebin Yang, Weinong Wang, Shuning Wang, Shangpin Peng, Fengyuan Lu, Xinming Wang, Zhehan Kan, Ruixu Zhang, Songyang Zhang, Sheng Yue, Yonglong Tian, Ju Ren

$T^5$: Twin-Critic Training for Token-Level Thoughts in Reinforcement Mid-Training

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SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning

Agentic reinforcement learning (RL) has become a critical stage in the post-training of large language models. Existing critic-free, group-relative methods estimate policy advantages from multiple rollouts, avoiding the substantial memory overhead of conventional proximal policy optimization (PPO) and achieving strong performance on long-horizon interactive tasks.