CLaaS: Continual learning as a service for sample efficient online learning
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:CLaaS: Continual learning as a service for sample efficient online learning
Abstract:Deployed large language model agents must adapt to distribution shift in dynamic environments. Ideally, adaptation can be performed from accumulated agent experiences and retain prior capabilities while transferring to future tasks. However, agent actions and environmental transitions can only be sampled once per scenario, as real-world environments cannot be trivially reset. To this end, we investigate an experiential and online continual learning setting in which agents learn from a stream of scenarios. We propose continual learning as-a-service (CLaaS), a system which enables agents to improve during deployment, abstracted behind a chat API. To increase sample efficiency, CLaaS stores rollouts in an experience replay buffer for gradient reuse during asynchronous training. We evaluate CLaaS on an adversarial task, demonstrating that parametric updates lead to superior forward transfer and less forgetting than in-context learning, with replay being a critical choice for sample efficiency.
| Comments: | 4 pages main content, 7 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.05559 [cs.LG] |
| (or arXiv:2606.05559v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05559
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models
Jun 5
-
ERRORQUAKE: Heavy-Tailed Error Severity Distributions in Open-Weight Large Language Models
Jun 5
-
Staged Factorial Screening for Budget-Constrained Micro-Pretraining
Jun 5
-
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
Jun 5
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.