When Updating Stops Being Learning: Rethinking LLM Self-Evolution via learnable information gain
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 i...
| Authors | Chenxu Wang, Chaozhuo Li, Xinze Shi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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