MetaX Weekly AI Paper Review Week 37 2025 -- Maximizing Reasoning Capability of Language Models Through Reinforcement Learning and New Training Paradigms, Expanding to Multimodal Environments While Resolving Fundamental Limitations Including Hallucination and Data Inconsistency: Sharing is Caring Efficient LM Post-Training with Collective RL Experience Sharing (arxiv 2509.08721): This paper presents a new approach solving parallelization difficulties in applying reinforcement learning to large language models. Unlike existing methods depending on large-scale computing resources, this paper proposes SAPO (Swarm sAmpling Policy Optimization) -- a completely distributed and asynchronous algorithm where each node in the network independently operates models and shares training experiences (rollouts) with other nodes, flexibly working regardless of hardware type or latency. Through this experience sharing, models achieve aha moment insights that enable genuine reasoning improvement rather than just pattern matching on training data. Additional paper reviews covered: new approaches to reducing LLM hallucination through better uncertainty calibration; multimodal training methods combining vision and language for improved cross-modal reasoning; data quality and consistency challenges in large-scale AI training; and advances in parameter-efficient fine-tuning methods that enable model adaptation without full retraining.