Dspy Rlm Module

RLM (Recurrent Language Model) Module

🎯 Trigger Conditions

Use when deciding between RLM, ReAct, and ChainOfThought for tool use or multi-step reasoning.

📚 Prerequisites

• dspy package installed
• Understanding of tool use patterns
• Clear reasoning requirements

🛠️ Module Comparison

1. ChainOfThought (CoT)

class CoTReasoning(dspy.Module):
    def __init__(self):
        self.reason = dspy.ChainOfThought("question -> reasoning -> answer")
    
    def forward(self, question):
        return self.reason(question=question)

When to use:

• Simple reasoning tasks
• No tool use required
• Single-step reasoning sufficient

2. ReAct (Reason + Act)

class ReActAgent(dspy.Module):
    def __init__(self, tools):
        self.react = dspy.ReAct(tools, max_steps=5)
    
    def forward(self, question):
        return self.react(question=question)

When to use:

• Tool use required
• Multi-step reasoning
• Dynamic decision making

3. RLM (Recurrent Language Model)

class RLMAgent(dspy.Module):
    def __init__(self):
        self.rlm = dspy.RLM(
            signature=YourSignature,
            n=3,  # Number of recurrences
            temperature=0.7
        )
    
    def forward(self, question):
        return self.rlm(question=question)

When to use:

• Complex multi-step reasoning
• Iterative refinement needed
• High-quality output required
• Can afford compute cost

📊 Decision Matrix

⚠️ Pitfalls

• RLM complexity: Can be overkill for simple tasks
• ReAct loops: Watch for infinite loops
• CoT depth: Limited to single reasoning step
• Compute budget: RLM requires significant resources

📖 References

• DSPy Modules
• ReAct Pattern
• RLM Documentation