Agentic Monte Carlo enables RL-style optimization of black-box LLM agents by sampling from the optimal policy posterior using Sequential Monte Carlo.
RL with KL penalties is better viewed as B ayesian inference
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Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Agentic Monte Carlo enables RL-style optimization of black-box LLM agents by sampling from the optimal policy posterior using Sequential Monte Carlo.