Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
Recent work shows RL retains prior capabilities more effectively than SFT. This paper extends to the mechanistic level, introducing differential circuit vulnerability to measure circuit degradation. On Qwen2.5-3B-Instruct for scientific QA, SFT adapts faster but causes greater circuit disruption and forgetting, while RL preserves circuits at the cost of slower adaptation. Results suggest circuit preservation explains RL's robustness against catastrophic forgetting.
Article intelligence
Key points
- SFT adapts quickly but disrupts internal circuits, leading to catastrophic forgetting.
- RL preserves more of the base model's circuits, resulting in less forgetting but slower task adaptation.
- New metric, differential circuit vulnerability, quantifies circuit degradation during fine-tuning.
- Study on Qwen2.5-3B-Instruct scientific QA model confirms mechanistic trade-off.
Why it matters
This matters because SFT adapts quickly but disrupts internal circuits, leading to catastrophic forgetting.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28860] Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
[Submitted on 21 May 2026]
Title:Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
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Abstract:Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capabilities more effectively than supervised fine-tuning (SFT), attributing this to policy-gradient updates remaining closer to the base policy \cite{shenfeld2025rl}. We extend this behavioral account to the mechanistic level and ask whether RL's advantage is mirrored by stronger preservation of internal computational circuits. We introduce differential circuit vulnerability, a head-level measure of how much a circuit degrades under fine-tuning, and use it to compare RL and SFT on Qwen2.5-3B-Instruct adapted to scientific question-answering. We find a clear mechanistic trade-off: SFT adapts more rapidly to the target task but produces substantially greater circuit disruption and forgetting of prior capabilities, whereas RL preserves a larger fraction of the base circuit at the cost of slower task adaptation. These findings suggest that circuit preservation may help explain why RL is more robust to catastrophic forgetting. We released our code here: this https URL.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2605.28860 [cs.LG]
(or arXiv:2605.28860v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28860
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Maheep Chaudhary [view email] [v1] Thu, 21 May 2026 19:03:57 UTC (919 KB)
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