dspy-simba-optimizer

# DSPy SIMBA Optimizer ## Goal Optimize DSPy programs using stochastic mini-batch sampling, output variability, self-reflective rules, and successful demonstrations. ## When to Use - Need lighter-weight alternative to GEPA - Have a numeric metric that captures task quality - Want introspective rules and demonstrations - Budget-conscious optimization (fewer eval calls) - Programs where few-shot examples aren't critical ## Related Skills - Alternative optimizers: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md), [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md) - Agent optimization: [dspy-react-agent-builder](../dspy-react-agent-builder/SKILL.md) - Evaluation: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Program to optimize | | `trainset` | `list[dspy.Example]` | Training examples | | `metric` | `callable` | Returns a numeric score | | `max_steps` | `int` | Number of optimization steps | | `bsize` | `int` | Mini-batch size | ## Outputs | Output | Type | Description | |--------|------|-------------| | `optimized_program` | `dspy.Module` | SIMBA-optimized program | ## Workflow ### Phase 1: Understand SIMBA **SIMBA** (Stochastic Introspective Mini-Batch Ascent): - Iterative prompt optimization with mini-batch sampling - Identifies challenging examples with high output variability - Generates self-reflective rules or adds successful dem...