MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation. When problems arrive sequentially, accumulating reusable experience across them can further improve performance. Existing memory-based methods either store whole-solution templates that generalize poorly to novel problems or use heuristic step-level selection that is not optimized for final-answer correctness. Learning selection policies requires large-scale training data and fixed action spaces, making such approaches unsuitable for test-time settings where memory expands incrementally and only limited supervision is available...
| Authors | Ruilin Tong, Dong Gong |
|---|---|
| Primary category | cs.CL |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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