dspy-simba
Small-Step Optimization with dspy.SIMBA
Guide the user through using dspy.SIMBA (Stochastic Introspective Mini-Batch Ascent) to optimize DSPy programs through incremental, targeted improvements rather than large sweeping changes.
What is dspy.SIMBA
dspy.SIMBA is a DSPy optimizer that improves programs by analyzing mini-batches of examples, identifying where the program struggles most, and making small targeted fixes -- either adding demonstrations or generating self-reflective rules. Instead of rewriting the entire prompt at once, SIMBA takes conservative steps, focusing on the examples with the highest output variability.
Key properties:
- Mini-batch driven -- samples small batches from the training set each iteration, rather than evaluating the entire dataset
- Variability-focused -- identifies the hardest examples by measuring output variability (gap between best and worst scores)
- Two improvement strategies -- adds few-shot demonstrations or generates introspective rules based on failure analysis
- Maintains a program pool -- keeps multiple candidate programs and probabilistically selects from the best performers
- Incremental by design -- each step makes a small, targeted change rather than overhauling the entire program
When to use SIMBA
Use dspy.SIMBA when:
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