GEPA Alignment Optimizer

MLflow provides the GEPA alignment optimizer using DSPy's implementation of GEPA (Genetic-Pareto). GEPA uses LLM-driven reflection to analyze execution traces and iteratively propose improved judge instructions based on human feedback.

Requirements

For alignment to work:

• Traces must contain human assessments (labels) with the same name as the judge
• Natural language feedback (rationale) is highly recommended for better alignment
• Minimum of 10 traces with human assessments required
• A mix of positive and negative labels is recommended

Basic Usage

See make_judge documentation for details on creating judges.

python

import mlflow
from mlflow.genai.judges import make_judge
from mlflow.genai.judges.optimizers import GEPAAlignmentOptimizer

judge = make_judge(
    name="politeness",
    instructions=(
        "Given a user question, evaluate if the chatbot's response is polite and respectful. "
        "Consider the tone, language, and context of the response.\n\n"
        "Question: {{ inputs }}\n"
        "Response: {{ outputs }}"
    ),
    feedback_value_type=bool,
    model="openai:/gpt-5-mini",
)

traces_with_feedback = mlflow.search_traces(return_type="list")

optimizer = GEPAAlignmentOptimizer(
    model="openai:/gpt-5-mini",
    max_metric_calls=100,
)
aligned_judge = judge.align(traces_with_feedback, optimizer)

Parameters

Parameter	Type	Default	Description
model	str	None	Model used for reflection. If None, uses the default model.
max_metric_calls	int	None	Maximum evaluation calls during optimization. If None, automatically set to 4x the number of training examples.
gepa_kwargs	dict	None	Additional keyword arguments passed directly to dspy.GEPA() for advanced configuration.

When to Use GEPA

GEPA is particularly effective when:

• Complex evaluation criteria: Your judge needs to understand nuanced, context-dependent quality standards
• Rich textual feedback: Human reviewers provide detailed explanations for their assessments
• Iterative refinement: You want the optimizer to learn from failures and propose targeted improvements

For simpler alignment tasks, consider using the default MemAlign optimizer.

Debugging

To debug the optimization process, enable DEBUG logging:

python

import logging

logging.getLogger("mlflow.genai.judges.optimizers.gepa").setLevel(logging.DEBUG)
aligned_judge = judge.align(traces_with_feedback, optimizer)