Online Experiential Learning for Language Models
- URL: http://arxiv.org/abs/2603.16856v1
- Date: Tue, 17 Mar 2026 17:57:49 GMT
- Title: Online Experiential Learning for Language Models
- Abstract summary: Online Experiential Learning (OEL) is a framework that enables language models to continuously improve from their own deployment experience.<n>OEL operates in two stages: first, transferable experiential knowledge is extracted and accumulated from interaction trajectories collected on the user side.<n>We evaluate OEL on text-based game environments across multiple model scales and both thinking and non-thinking variants.
- Score: 99.90826536842337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 are iterated to form an online learning loop, where the improved model collects higher-quality trajectories that yield richer experiential knowledge for subsequent rounds. We evaluate OEL on text-based game environments across multiple model scales and both thinking and non-thinking variants. OEL achieves consistent improvements over successive iterations, enhancing both task accuracy and token efficiency while preserving out-of-distribution performance. Our analysis further shows that extracted experiential knowledge is significantly more effective than raw trajectories, and that on-policy consistency between the knowledge source and the policy model is critical for effective learning.
Related papers
- Self-Improving Large Language Models via Progressive Experience Evolution [52.03479747358687]
We propose textbfSPEE (textbfSelf-textbfProgressive textbfExperience textbfEvolution), a unified post-training framework that sequentially performs explicit experience evolution followed by implicit policy optimization.<n> Experiments on five mathematical reasoning benchmarks demonstrate that SPEE consistently outperforms both test-time and training-time self-evolution baselines across three model scales.
arXiv Detail & Related papers (2026-08-03T12:27:32Z) - On-Policy Context Distillation for Language Models [92.82835176360864]
We propose On-Policy Context Distillation (OPCD), a framework that bridges on-policy distillation with context distillation.<n>We demonstrate the effectiveness of OPCD on two important applications: experiential knowledge distillation and system prompt distillation.
arXiv Detail & Related papers (2026-02-12T18:58:28Z) - Decouple to Generalize: Context-First Self-Evolving Learning for Data-Scarce Vision-Language Reasoning [41.523848964102]
Recent vision-language models (VLMs) achieve remarkable reasoning through reinforcement learning (RL)<n>RL provides a feasible solution for realizing continuous self-evolving large vision-language models (LVLMs) in the era of experience.<n>Existing strategies such as synthetic data and self-rewarding mechanisms suffer from limited distributions and alignment difficulties.<n>We propose DoGe, a dual-decoupling framework that guides models to first learn from context rather than problem solving.
arXiv Detail & Related papers (2025-12-07T13:17:31Z) - Experience Scaling: Post-Deployment Evolution For Large Language Models [44.48142891798125]
We propose experience scaling, a framework for continuous post-deployment evolution for large language models (LLMs)<n>We validate the framework in simulated real-world scenarios involving generalization to previously unseen but related tasks, repetitive queries, and over-saturated knowledge stores.<n>Results demonstrate that structured post-deployment learning can extend LLM capabilities beyond the limits of static human-generated data.
arXiv Detail & Related papers (2025-09-23T08:04:58Z) - Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning [23.900888224619]
We show that curriculum learning consistently improves convergence in early and mid-training phases.<n>We identify compression ratio, lexical diversity, and readability as effective difficulty signals across settings.
arXiv Detail & Related papers (2025-06-12T21:06:57Z) - UniErase: Towards Balanced and Precise Unlearning in Language Models [69.04923022755547]
Large language models (LLMs) require iterative updates to address the outdated information problem.<n>UniErase is a novel unlearning framework that demonstrates precision and balanced performances between knowledge unlearning and ability retaining.
arXiv Detail & Related papers (2025-05-21T15:53:28Z) - Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning [65.85335291827086]
This paper tries to learn and understand underlying semantic variations from distracting videos via offline-to-online latent distillation and flexible disentanglement constraints.<n>We pretrain the action-free video prediction model offline with disentanglement regularization to extract semantic knowledge from distracting videos.<n>For finetuning in the online environment, we exploit the knowledge from the pretrained model and introduce a disentanglement constraint to the world model.
arXiv Detail & Related papers (2025-03-11T13:50:22Z) - LLM Post-Training: A Deep Dive into Reasoning Large Language Models [131.10969986056]
Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications.<n>Post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations.
arXiv Detail & Related papers (2025-02-28T18:59:54Z) - Training a Generally Curious Agent [77.61142660542599]
Paprika is a fine-tuning approach that enables language models to develop general decision-making capabilities.<n>Paprika teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates.<n>Results suggest a promising path towards AI systems that can autonomously solve sequential decision-making problems.
arXiv Detail & Related papers (2025-02-24T18:56:58Z) - Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning [79.46570165281084]
We propose a Multi-Stage Knowledge Integration network (MulKI) to emulate the human learning process in distillation methods.
MulKI achieves this through four stages, including Eliciting Ideas, Adding New Ideas, Distinguishing Ideas, and Making Connections.
Our method demonstrates significant improvements in maintaining zero-shot capabilities while supporting continual learning across diverse downstream tasks.
arXiv Detail & Related papers (2024-11-11T07:36:19Z) - An Active Learning Framework for Inclusive Generation by Large Language Models [32.16984263644299]
Large Language Models (LLMs) generate text representative of diverse sub-populations.<n>We propose a novel clustering-based active learning framework, enhanced with knowledge distillation.<n>We construct two new datasets in tandem with model training, showing a performance improvement of 2%-10% over baseline models.
arXiv Detail & Related papers (2024-10-17T15:09:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.