Topic: Reinforcement Learning

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1. Minimax M2.5: Scaling RL for Industrial-Grade Agentic AI

   Paper • Feb 16, 2026 • arXiv • MiniMax Research Team

   Training agents for industrial-scale deployment requires extreme stability and data throughput. We present Minimax M2.5, a model trained using a novel asynchronous RL architecture designed to proce...

2. MASPO: Robust and Sample-Efficient LLM Reasoning via Unified Policy Optimization

   Paper • Feb 19, 2026 • arXiv • Xiaoliang Fu, Jiaye Lin, Yangyi Fang

   Policy optimization for Large Language Models often suffers from gradient instability and reward signal unreliability, particularly in mathematical and verifiable reasoning tasks. We introduce MASP...

3. KLong: Training LLM Agents for Extremely Long-horizon Tasks

   Paper • Feb 19, 2026 • arXiv • Yue Liu, Zhiyuan Hu, Flood Sung

   Current LLM agents frequently fail in tasks requiring hundreds of steps due to error accumulation and context overflow. We introduce KLong, an agentic framework that utilizes 'Trajectory-Splitting ...