Experience Replay to Stop Catastrophic Forgetting in LLMs

Thu, 29 Oct 2026 16:00:00 +0900

Interleaving a rehearsal buffer with new domain trajectories prevents directional gradient interference and catastrophic forgetting

Key Takeaways

The dual-memory architecture: Mitigating experience replay catastrophic forgetting llm risks requires operationalizing Complementary Learning Systems (CLS), interleaving fast episodic task acquisition with slow consolidation of prior knowledge.
Root causes of representation collapse: Fine-tuning low-rank adapters exclusively on specialized domain trajectories can sometimes create directional gradient interference, overriding foundational reasoning, prompt comprehension, and code syntax.
Rehearsal buffer mixing ratios: Interleaving a modest 10% rehearsal buffer from pre-cutoff general datasets helps prevent representational drift, maintaining benchmarks without diluting domain adaptation velocity.

TL;DR

When developers continually fine-tune language models on specialized tasks, parameter updates rapidly overwrite foundational knowledge. Understanding experience replay catastrophic forgetting llm dynamics requires looking at how biological memory stabilizes learning across shifting environments. Drawing on complementary learning systems continual learning theory, experience replay interleaves new environmental trajectories with historical or pre-training distributions. While most LoRA finetunes do not require a rehearsal buffer, if the LoRA data creates directional gradient interference it can still result in catastrophic forgetting. In this case injecting a small ratio (around 10%) of general rehearsal data across virtual epochs bounds gradient drift. Consequently, the model preserves baseline reasoning while acquiring aggressive, highly optimized domain capabilities.

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Experience Replay to Stop Catastrophic Forgetting in LLMs

Key Takeaways

TL;DR