
RL's Scaling Problem: Representation Depth Matters, Says NeurIPS 2025
NeurIPS 2025 highlighted that AI progress is increasingly limited by architectural design, training dynamics, and evaluation strategies, rather than solely by model size. Key papers challenged assumptions about scaling, reinforcement learning, attention mechanisms, and generative models, emphasizing the need for innovative approaches to overcome these constraints and improve AI system development.
















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