Temporal Dynamics of Model Alignment
How safety properties evolve across training iterations, deployment cycles, and shifting user populations
Alignment Stability Under Iterative Training
The alignment properties of large language models are not static. They emerge from the interaction of pre-training data distributions, supervised fine-tuning examples, and reinforcement learning reward signals, and they continue to evolve as models undergo additional training iterations in response to deployment feedback. Understanding the temporal dynamics of these properties is essential for maintaining safety guarantees over the lifecycle of a deployed model. A model that passes all safety evaluations at release may gradually degrade in its refusal behavior as it is fine-tuned on new data that shifts the underlying decision boundaries. This phenomenon, which we term alignment drift, has been observed across multiple model families and training paradigms.
Measuring alignment drift requires longitudinal evaluation frameworks that go beyond point-in-time safety benchmarks. Traditional evaluation approaches test a model at a single checkpoint and declare it safe or unsafe based on aggregate metrics. This approach fails to capture the trajectory of safety properties over time and cannot distinguish between a model that is stably aligned and one that happens to pass evaluations at the measured checkpoint but is trending toward failure. Effective longitudinal evaluation requires repeated measurement across training iterations, using consistent evaluation sets that are isolated from the training pipeline to prevent benchmark contamination.
Distributional Shift in User Populations
A second dimension of temporal dynamics concerns the evolving distribution of user inputs. Even when a model's weights are frozen, the effective safety posture of the system changes as the user population shifts. Early adopters of a language model may interact with it in ways that are well-represented in the safety training data. As adoption broadens, the model encounters input distributions that diverge from what was anticipated during safety training. This divergence can expose latent vulnerabilities that were present but unexpressed during initial deployment. The adversarial robustness of a system is therefore a function not only of the model's internal properties but also of the external context in which it operates.
Cross-lingual distributional shift presents a particularly acute challenge. Models that exhibit strong safety properties in English may show degraded alignment in languages that were underrepresented in the RLHF training data. As language models are deployed globally, the fraction of interactions occurring in low-resource languages increases, and the gap between evaluated safety performance and actual safety performance widens. Addressing this requires both multilingual safety training data and evaluation frameworks that explicitly test for cross-lingual robustness.
Feedback Loops and Alignment Oscillation
The interaction between model deployment and subsequent training creates feedback loops that can produce oscillatory alignment behavior. When a model is updated based on user reports of safety failures, the resulting fine-tuning may overcorrect, producing a model that refuses benign requests that are superficially similar to the reported failures. This overcorrection generates a new wave of user complaints about excessive refusal, which in turn drives further fine-tuning in the opposite direction. The result is an oscillation between under-refusal and over-refusal that never converges on a stable equilibrium. Breaking this cycle requires moving beyond reactive fine-tuning toward proactive alignment strategies that anticipate the distributional consequences of deployment feedback.
Formal methods offer one promising approach to characterizing alignment stability. By modeling the alignment training process as a dynamical system, researchers can analyze convergence properties, identify fixed points, and predict the conditions under which alignment oscillation is likely to occur. Early results in this direction suggest that the stability of alignment training is sensitive to the learning rate schedule, the composition of the reward model training set, and the relative weighting of helpfulness and harmlessness objectives. These findings have practical implications for the design of training pipelines that produce models with durable, rather than transient, safety properties.