Self-Preservation in Language Models
Quick Answer
The study explores self-preservation in language models through task banks, showing that continual instruction tuning with Qwen models enhances retention of prior capabilities.
Quick Take
Key findings indicate that generated replay can mitigate forgetting, and informed task selection improves retention rates, particularly in larger models.
Key Points
- Task banks organize tasks to guide replay generation in frozen models.
- Continual instruction tuning with Qwen models shows improved retention of correct answers.
- Generated replay reduces forgetting on tasks not included in the task bank.
- Informed task selection enhances retention over random selection in larger models.
- Replacing new-task examples with replay data improves preservation under fixed budgets.
DeepSignal Analysis
What happened
The study investigates self-preservation in language models, specifically through the use of Task Banks and continual instruction tuning with Qwen models. It finds that generated replay can help mitigate forgetting and that informed task selection enhances retention rates, particularly in larger models.
Key evidence
- The research indicates that continual instruction tuning with Qwen models can enhance the retention of prior capabilities.
- Generated replay has been shown to reduce forgetting on tasks that are not included in the Task Banks.
- The study reveals that informed task selection improves mean retention rates over random selection, especially in larger models like the 4B variant.
Why it matters
Understanding self-preservation in language models is crucial for improving their performance in continual learning scenarios. The ability to mitigate forgetting and enhance retention through replay and informed task selection could lead to more robust AI systems that maintain their capabilities over time. This has implications for various applications where consistent performance is required.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Continual learning (CL) seeks to adapt pretrained models while preserving prior capabilities. Yet the tasks supplied for preservation cover only part of what a model knows, while forgetting can extend beyond them. Recent work shows that language models can supplement this limited evidence by generating useful replay without access to their original training data. This raises a question about self-preservation: can a model help choose preservation targets that protect its own capabilities more broadly? To study this question, we organize task descriptions and their examples into Task Banks, whose entries can be selected and combined to guide replay generation by a frozen model. Continual instruction tuning with Qwen models of varying sizes yields three findings. First, the new task and learning rate shape which prior capabilities degrade, while tracking previously correct answers reveals losses that average accuracy can conceal. Second, generated replay reduces forgetting on tasks outside the bank. Combining partial banks strengthens this protection, improving mean retention of previously correct answers over either bank alone across the tested sizes. Third, the model can also help choose replay targets: selection informed by the new task improves mean held-out retention over random selection at 4B, with similar new-task gains, although smaller models show no corresponding advantage. Under a fixed example budget, however, replacing new-task examples with replay improves preservation at the cost of smaller new-task gains. Together, these findings show that models can support broader self-preservation by generating replay data and, in some settings, choosing which tasks to replay.
| Comments: | 25 pages, 12 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12602 [cs.CL] |
| (or arXiv:2610.12602v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12602 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dongkyu Cho [view email]
[v1]
Thu, 8 Oct 2026 18:24:02 UTC (356 KB)
— Originally published at arxiv.org
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