Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control

Researchers propose Anchored Learning, a new fine-tuning method that prevents catastrophic forgetting in large language models by controlling distributional drift through a dynamically evolving reference anchor. The technique achieves near-optimal performance gains while reducing degradation from over 53% to under 5% on benchmark tasks.

The stability of large language models during post-training remains a critical challenge in AI development. When models undergo supervised fine-tuning on specific tasks, they frequently experience catastrophic forgetting—a degradation of previously learned capabilities. Anchored Learning addresses this by introducing a framework that treats fine-tuning as a series of localized trust-region updates rather than global optimization, using an interpolated anchor between the current model and a frozen reference distribution.

This research builds on growing recognition that excessive distributional drift during optimization drives catastrophic forgetting. Previous approaches attempted to preserve knowledge through fixed reference distributions, but Anchored Learning innovates by making the reference dynamic, allowing it to gradually shift alongside the model. The theoretical contribution proves linear KL-divergence bounds per iteration, providing formal guarantees about training stability.

The practical implications are substantial for production AI systems. On three benchmark datasets—iGSM, MedCalc, and IFEval—Anchored Learning consistently achieves Pareto-optimal trade-offs between task improvement and capability retention. Standard fine-tuning suffers catastrophic performance drops exceeding 50% while gaining only marginal improvements; Anchored Learning nearly eliminates this degradation while maintaining strong gains.

For AI developers and practitioners, this method directly addresses a fundamental bottleneck in deploying specialized language models. Organizations can now fine-tune models for specific domains or tasks without sacrificing general capabilities. As AI systems become increasingly integrated into critical applications, techniques that guarantee stability during customization become essential infrastructure rather than optional improvements.

• →Anchored Learning prevents catastrophic forgetting by controlling distributional drift during fine-tuning through a dynamically evolving reference anchor.
• →The method reduces performance degradation from over 53% to under 5% while maintaining near-optimal task improvements across benchmark datasets.
• →Theoretical analysis proves linear KL-divergence upper bounds per iteration, ensuring stable model distribution transitions.
• →The approach transforms fine-tuning into localized trust-region updates rather than global optimization, improving both stability and performance simultaneously.
• →This technique has immediate practical value for organizations deploying domain-specific language models while preserving general capabilities.