Pretraining scaling laws and reinforcement learning (RL) scaling laws represent two distinct approaches to understanding how model performance improves with increased computational resources. While both exhibit smooth scaling trends, they differ fundamentally in their mathematical structure, measurement consistency, and practical optimization challenges. Understanding these differences is critical for researchers and practitioners designing large-scale AI systems, as the choice between pretraining-based and RL-based approaches has profound implications for resource allocation and model development strategies.
Pretraining scaling laws describe the relationship between computational resources and model performance in large language models trained on next-token prediction objectives. These laws follow well-established power law equations that provide consistent, predictable relationships between compute, parameters, and test loss 1).
The standardized form typically follows: Loss = a × N^(-α), where N represents compute and α is a scaling exponent typically around 0.07-0.08 for large models. This consistency enables straightforward optimization strategies 2). Organizations can reliably predict performance improvements before deploying expensive training runs, allowing for principled decisions about model size, training duration, and data allocation.
Pretraining metrics remain standardized across implementations: test loss serves as the primary measurement unit, enabling direct comparison across different architectures, datasets, and organizations. This uniformity has allowed the field to converge on unified scaling laws that apply broadly to transformer-based language models, regardless of specific implementation details.
Reinforcement learning scaling laws, by contrast, exhibit substantially greater complexity and heterogeneity. Rather than following a single standardized equation, RL scaling relationships vary significantly depending on the specific task, reward structure, and learning algorithm employed. Researchers observe similar smooth scaling trends with increased compute—performance genuinely improves as resources increase—yet the underlying mathematical structure remains inconsistent and task-dependent 3).
The measurement challenge in RL scaling laws stems from the diversity of evaluation metrics. Pretraining uses a single metric (test loss), while RL applications may employ win rates, cumulative rewards, human preference scores, benchmark performance, or domain-specific evaluation metrics. A model trained with reinforcement learning from human feedback (RLHF) might show different scaling characteristics when measured by human preference than when measured by objective task performance. This metric diversity means each RL application requires custom scaling law analysis rather than applying a universal formula.
Additionally, RL scaling laws incorporate additional variables absent from pretraining analysis: reward signal quality, optimization algorithm choice, exploration-exploitation tradeoffs, and policy gradient variance. These factors introduce non-monotonic relationships and local optima that complicate simple power law modeling.
The structural differences between pretraining and RL scaling laws create practical challenges for optimization. Pretraining optimization benefits from predictable scaling laws: organizations can extrapolate performance from smaller pilot runs and plan large-scale training with confidence. The standardized test loss metric allows direct comparison of scaling coefficients across papers and implementations.
RL optimization, conversely, requires substantially more empirical exploration. Teams cannot reliably predict how performance will scale before conducting expensive full-scale training runs, since the specific task, reward formulation, and algorithmic details all affect scaling characteristics. This empirical uncertainty necessitates more conservative resource allocation and limits the ability to extrapolate findings across different RL applications 4).
Despite these challenges, both scaling law regimes exhibit genuine scaling trends with increased compute. The smoothness of RL scaling curves, even when heterogeneous across tasks, indicates that fundamental learning dynamics operate similarly—performance genuinely improves predictably with resources—but the mathematical characterization remains problem-specific rather than universal.
Recent work attempts to unify RL scaling law understanding through several approaches. Researchers investigate whether common scaling exponents exist across RL domains, explore how reward model quality affects downstream scaling characteristics, and develop better theoretical frameworks for understanding why RL scaling laws remain stubbornly diverse 5).
The distinction between pretraining and RL scaling laws carries important implications for AI system design. Organizations investing in scaling compute resources must account for the measurement and optimization challenges specific to RL, rather than assuming pretraining-derived insights transfer directly. As reinforcement learning becomes increasingly central to advanced AI systems—from alignment techniques to capability development—developing more standardized RL scaling law characterizations remains an open research priority.