Online Experiential Learning for Language Models
The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the rich experience accumulated during real-world deployment entirely unexploited. We propose Online Experiential Learning (OEL), a framework that enables language models to continuously improve from their own deployment experience. OEL operates in two stages: first, transferable experiential knowledge is extracted and accumulated from interaction trajectories collected on the user side; second, this knowledge is consolidated into model parameters via on-policy context distillation, requiring no access to the user-side environment. The two stages...
| Authors | Tianzhu Ye, Li Dong, Qingxiu Dong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 29 June 2026 |
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