When Updating Stops Being Learning: Rethinking LLM Self-Evolution via learnable information gain

Abstract:Self-evolution lets large language models (LLMs) improve iteratively using their own generated data, but often suffers from self-evolution degeneration: performance improves, plateaus, then declines. Existing methods address this issue at the component level, targeting either the Questioner or the Solver, and overlook that self-evolution is a tightly coupled system. We propose a holistic framework based on learnable information gain, which measures how much novel, parameterizable information a round provides relative to the previous round. Theoretically, this gain equals the Kullback-Leibler divergence between the two rounds' data distributions plus their entropy change. Practically, it is estimated by fitting a small language model to the previous round and scoring new data via negative log-likelihood. Based on this diagnostic, we propose ATRI (Adaptive Training Regulation via Information-gain), which reweights samples within a round and halts training across rounds when information gain remains low. Experiments on popular datasets demonstrate the superiority of our proposal.

Computation and Language (cs.CL)

arXiv:2609.36535 [cs.CL]

(or arXiv:2609.36535v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.36535 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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