Why reinforcement learning plateaus without representation depth (NeurIPS 2025) (venturebeat.com)

At NeurIPS 2025, several groundbreaking papers challenged longstanding assumptions in AI/ML research, emphasizing that advancements are more about system design and architecture than just scaling model size. One key paper introduced Infinity-Chat, a benchmark evaluating the diversity of outputs in large language models (LLMs), revealing a trend toward homogeneity in creative tasks across different models. This suggests that companies need to prioritize diversity in AI outputs, as excessive preference tuning could lead to predictable and biased responses. Another significant discovery highlighted that reinforcement learning (RL) could scale effectively by increasing network depth, with performance improvements achieved by using up to 1,000 layers paired with stable training methods. This contradicts the idea that RL is constrained by data and rewards alone, indicating that architectural choices are critical for scaling. Additionally, researchers showed that diffusion models' ability to generalize rather than memorize is influenced by training dynamics rather than sheer parameter count. Altogether, these findings shift the focus from merely building larger models to optimizing the overall system design, stressing that innovation will hinge on understanding these complex dynamics in AI development.