The NeurIPS (Neural Information Processing Systems) 2025 conference, held from November 30th to December 7th, 2025, in San Diego and Mexico City, revealed pivotal insights into advancing reinforcement learning (RL). A key takeaway, consistent with the VentureBeat article's premise, is that significant representation depth is fundamental to overcoming performance plateaus in RL. Historically, most RL models have employed shallower architectures, typically ranging from 2 to 5 layers. However, groundbreaking research presented at NeurIPS 2025 demonstrated that scaling network depth up to 1024 layers can substantially amplify performance, yielding improvements of 2x to 50x in self-supervised contrastive RL algorithms. This indicates a paradigm shift, suggesting that robust representation learning through architectural depth, akin to advancements in language and vision models, is crucial for RL, especially when combined with self-supervised methods for learning effective state, action, and future state representations. Such deep networks enable agents to explore and master goal-conditioned tasks in unsupervised settings without explicit rewards or human demonstrations. Additionally, the conference featured critical discussions on whether RL truly fosters novel reasoning capabilities in large language models (LLMs) or primarily optimizes their pre-existing capacities. The role of enhanced inference-time strategies in boosting RL performance by allowing AI to engage in a 'thought' process before action was also a notable area of discussion. VentureBeat is a respected outlet for technology news, and the article's claims align with verified outcomes and prominent research from NeurIPS 2025.