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arXiv Computation and Language By Chenxu Wang, Chaozhuo Li, Xinze Shi, Songyang Liu, Kyrie You Wu, Ziluowen Luo, Shun Zhang, Chenxi Li, Litian Zhang

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

Read the original on arXiv Computation and Language →

The paper introduces a holistic framework for large language model self‑evolution that uses learnable information gain to assess the novelty of each training round. Information gain is theoretically linked to the Kullback‑Leibler divergence and entropy change between successive data distributions, and practically estimated by fitting a small language model and scoring new data with negative log‑likelihood. The proposed ATRI method reweights samples within a round and stops training across rounds when information gain is low, and experiments on popular datasets show its effectiveness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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