Learning to Self-Evolve
We introduce Learning to Self-Evolve (LSE), a reinforcement learning framework that trains large language models (LLMs) to improve their own contexts at test time. We situate LSE in the setting of test-time self-evolution, where a model iteratively refines its context from feedback on seen problems to perform better on new ones. Existing approaches rely entirely on the inherent reasoning ability of the model and never explicitly train it for this task. LSE reduces the multi-step evolution problem to a single-step RL objective, where each context edit is rewarded by the improvement in downstream performance. We pair this objective with a tree-guided evolution loop. On Text-to-SQL generatio...
| Authors | Xiaoyin Chen, Canwen Xu, Yite Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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