Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
arXiv:2601.18510v2 Announce Type: replace Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment. Conventional reinforcement learning (RL) offers a solution but incurs prohibitive computational costs and the risk of catastrophic forgetting. We introduce Just-In-Time Reinforcement Learning (JitRL), a training-free framework that enables test-time policy optimization without any gradient updates. JitRL maintains a dynamic, non-parametric memory of experiences and retrieves relevant tr
The increasing sophistication of LLM agents highlights the critical need for continuous adaptation post-deployment without the prohibitive costs of traditional retraining, making solutions like JitRL highly relevant.
This development addresses a fundamental limitation of current LLM agents, enabling them to adapt and optimize in real-time environments without gradient updates, thereby accelerating their practical application and autonomy.
LLM agents can now continuously learn and improve their policies during deployment without catastrophic forgetting or expensive retraining cycles, opening new avenues for their use in dynamic settings.
- · AI developers
- · LLM-powered SaaS companies
- · Robotics
- · Edge AI
- · Traditional RL fine-tuning services
- · Compute-intensive model retraining infrastructure
LLM agents become more robust and capable in real-world, constantly evolving environments.
The cost-efficiency of deploying adaptable AI agents increases, accelerating their integration into complex systems.
This could lead to a proliferation of highly autonomous AI agents capable of sustained operation and learning in unstructured environments.
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Read at arXiv cs.LG