AIBullisharXiv – CS AI · 9h ago7/10
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Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Researchers propose Agentic Monte Carlo (AMC), a novel method for optimizing black-box LLM agents without API access by using Sequential Monte Carlo sampling to steer agents toward optimal behavior. The technique bridges the gap between reinforcement learning and Bayesian inference, demonstrating competitive performance against RL baselines while maintaining the black-box model architecture.