6arXiv cs.CL (Computation and Language)·
LLMs can extract and reuse experiential abstractions to improve reasoning, study finds
Researchers investigate whether LLMs can distill solution traces into reusable natural-language abstractions (strategies, reminders) and apply them to future problems, analogous to human experiential learning. Using the MATH training set, abstractions are extracted by either a stronger teacher model or the LLMs themselves into a retrievable library, then applied via inference-time retrieval or RL with abstraction-augmented prompts. Both modes improve performance on mathematical and logical reasoning benchmarks, with self-extracted abstractions matching teacher-extracted ones. The framework generalizes across datasets and models, suggesting a promising direction for continual self-improvement in LLMs.
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