Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates

TL;DR

This paper introduces FINCH, a loss-adaptive learning-rate schedule that significantly reduces catastrophic forgetting during fine-tuning large language models without sacrificing task performance.

Contribution

The paper proposes a novel loss-adaptive learning-rate schedule, FINCH, which effectively controls forgetting during fine-tuning by adjusting learning rates based on batch loss.

Findings

FINCH reduces forgetting by 93% on average across benchmarks.

On Qwen3-4B, FINCH cuts TruthfulQA degradation by 5x.

FINCH better preserves model confidence calibration.

Abstract

Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as catastrophic forgetting. Existing methods mitigate this by modifying the fine-tuning objective to suppress high-loss tokens or sequences, but these tokens are essential for learning new tasks, especially those with poor pretraining coverage. In such settings, hard tokens should still contribute to learning, so forgetting must be controlled without suppressing them. We identify a simple mechanism for doing so: per-step forgetting is bounded by the product of the learning rate and the square root of the current training loss. This suggests that high-loss batches are especially prone to inducing forgetting. Motivated by this observation, we introduce FINCH, a loss-adaptive learning-rate schedule that reduces the learning rate on high-loss batches and increases it as the model converges, while leaving the fine-tuning objective unchanged. Across knowledge acquisition, science, and low-resource language adaptation benchmarks, FINCH reduces forgetting by 93% on average while matching the task performance of standard fine-tuning. On Qwen3-4B knowledge acquisition, FINCH cuts TruthfulQA degradation by 5x and reverses HaluEval degradation, while better preserving confidence calibration. Overall, our results show that learning-rate schedules are an effective tool to shape model behavior during fine-tuning, beyond just target-task optimization.