Notes to Self: Can LLMs Benefit from Experiential Abstractions?
Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effectively. We study whether Large Language Models (LLMs) can similarly benefit from such experiential abstractions. From LLMs' solution traces on the MATH training set, a stronger teacher or the LLMs themselves extract natural-language abstractions into a retrievable library. We explore two usage modes: (1) inference-time retrieval and (2) reinforcement learning (RL) with abstraction-augmented training prompts. Experiential abstractions improve LLM performance on mathematical and logical reasoning benchmarks. Self-extracted abstractions match t...
| Authors | Chang Liu, Xinyu Li, Artur Dubrawski |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 30 August 2026 |
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