# Experimental RL Optimization for DSPy

This section explores cutting-edge reinforcement learning (RL) approaches for optimizing DSPy programs. These experimental techniques represent the frontier of AI program optimization, combining the power of RL with DSPy's modular programming paradigm to achieve even better performance on complex tasks.

## Advanced RL Optimization Techniques

### [RL for Privacy-Conscious Delegation](../rl_papillon/index.ipynb)
Explore how reinforcement learning can optimize privacy-conscious AI systems. This tutorial demonstrates how RL agents can learn to balance task performance with privacy constraints, making intelligent decisions about when and how to delegate sensitive operations.

### [RL for Multi-Hop Research](../rl_multihop/index.ipynb)
Learn to apply reinforcement learning to multi-hop reasoning tasks. This advanced tutorial shows how RL can optimize the search strategy in complex information retrieval scenarios, learning to navigate through multiple information sources more effectively.
