Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

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• JitRL enables test-time policy optimization without any gradient updates.method 95%
• JitRL's additive update rule to modulate LLM logits is the exact closed-form solution to the KL-constrained policy optimization objective.technical 90%
• JitRL establishes a new state-of-the-art among training-free methods on WebArena and Jericho benchmarks.result 85%
• JitRL outperforms computationally expensive fine-tuning methods like WebRL.result 80%
• JitRL reduces monetary costs by over 30 times compared to fine-tuning methods.market 90%
• Conventional RL for continual adaptation in LLM agents incurs prohibitive computational costs and risk of catastrophic forgetting.limitation 85%
• LLM agents inherently struggle with continual adaptation due to frozen weights after deployment.limitation 90%
• JitRL maintains a dynamic, non-parametric memory of experiences to retrieve relevant trajectories for on-the-fly advantage estimation.method 90%

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