Ablation for all CePO steps

src/gepa/adapters/cepo_adapter/cepo_coding_adapter.py

@@ -98,6 +98,12 @@ def evaluate(
         if not candidate:
             raise ValueError("Candidate must contain at least one component text.")
 
+        # Ensure all required prompts are in candidate
+        required_prompts = ["cepo_planning_prompt", "cepo_execution_prompt", "cepo_reflection_prompt"]
+        for prompt in required_prompts:
+            if prompt not in candidate:
+                raise ValueError(f"Candidate must contain '{prompt}'")
+
         n = len(batch)
 
         # ---------- Phase 1: parallelize ONLY the LLM calls (cepo_simple) ----------
@@ -108,8 +114,8 @@ def _run_cepo(idx: int, data: CepoCodingDataInst) -> Tuple[int, str]:
             final_output, plans, executions = cepo_simple(
                 system_prompt="",
                 planning_prompt=candidate["cepo_planning_prompt"],
-                execution_prompt=global_candidate["cepo_execution_prompt"],
-                reflection_prompt=global_candidate["cepo_reflection_prompt"],
+                execution_prompt=candidate["cepo_execution_prompt"],
+                reflection_prompt=candidate["cepo_reflection_prompt"],
                 question=data["question"],
                 client=self.client,
                 model=self.model,

src/gepa/examples/cepo/train_cepo.py

@@ -152,8 +152,12 @@ def reflection_lm(prompt: str):
     print("Initial prompt test set score", sum(initial_test_scores) / len(initial_test_scores))
 
     # Training and validation
+    candidates = {"cepo_planning_prompt": candidate["cepo_planning_prompt"],
+                "cepo_execution_prompt": candidate["cepo_execution_prompt"],
+                "cepo_reflection_prompt": candidate["cepo_reflection_prompt"]}
+
     optimized_results = optimize(
-        seed_candidate={"cepo_planning_prompt": candidate["cepo_planning_prompt"]},
+        seed_candidate=candidates,
         trainset=trainset,
         valset=valset,
         adapter=cepo_adapter,

src/gepa/strategies/instruction_proposal.py

@@ -24,29 +24,36 @@ class InstructionProposalSignature(Signature):
 
 # Provide the new instructions within ``` blocks."""
 
-    prompt_template = """I provided an assistant with the following PLANNING instruction:
+    prompt_template = """I provided an assistant with the following {PROMPT_TYPE} instruction:
 ```
 <curr_instructions>
 ```
 
-The following are examples of different task inputs, the assistant’s responses, and feedback on how the PLANNING could be improved:
+The following are examples of different task inputs, the assistant’s responses, and feedback on how the {PROMPT_TYPE} could be improved:
 ```
 <inputs_outputs_feedback>
 ```
 
-Your task is to propose a revised PLANNING instruction for the assistant.
+Your task is to propose a revised {PROMPT_TYPE} instruction for the assistant.
 
-Guidelines:
-- Focus ONLY on improving the *structure and clarity of planning* (e.g., require decomposition into steps, explicit input/output contracts, consideration of boundary cases, and a self-check).
-- DO NOT include any dataset-specific content, numeric constraints, input/output formats, examples, or code.
-- Keep the instruction general so it can apply to many tasks.
-- The revision should be concise, clear, and in natural language.
+Guidelines {GUIDELINES}
 
 Return only the new instruction inside ``` blocks."""
 
-    input_keys = ["current_instruction_doc", "dataset_with_feedback"]
+    input_keys = ["current_instruction_doc", "dataset_with_feedback", "prompt_type"]
     output_keys = ["new_instruction"]
 
+    @classmethod
+    def get_guidelines(cls, prompt_type):
+        guidelines = {
+            "PLANNING": "- Focus ONLY on improving the *structure and clarity of planning* (e.g., require decomposition into steps, explicit input/output contracts, consideration of boundary cases, and a self-check).\n- DO NOT include any dataset-specific content, numeric constraints, input/output formats, examples, or code.\n- Keep the instruction general so it can apply to many tasks.\n- The revision should be concise, clear, and in natural language.",
+
+            "EXECUTION": "- Focus ONLY on improving how the assistant should *execute a plan* (e.g., methodical implementation, validation of intermediate steps, error checking).\n- Emphasize careful execution especially for steps where confidence is lower.\n- DO NOT include any dataset-specific content, numeric constraints, input/output formats, examples, or code.\n- Keep the instruction general so it can apply to many tasks.\n- The revision should be concise, clear, and in natural language.",
+
+            "REFLECTION": "- Focus ONLY on improving how the assistant should *reflect on and validate* their work (e.g., reviewing for inconsistencies, verifying solutions, refining approaches).\n- Emphasize careful review of the entire problem-solving process.\n- DO NOT include any dataset-specific content, numeric constraints, input/output formats, examples, or code.\n- Keep the instruction general so it can apply to many tasks.\n- The revision should be concise, clear, and in natural language."
+        }
+        return guidelines.get(prompt_type, guidelines["PLANNING"])
+
     @classmethod
     def prompt_renderer(cls, input_dict: dict[str, str]) -> str:
         def format_samples(samples):
@@ -79,10 +86,16 @@ def convert_sample_to_markdown(sample, examplenum):
                 return s
 
             return "\n\n".join(convert_sample_to_markdown(sample, i + 1) for i, sample in enumerate(samples))
+
+        prompt_type = input_dict.get("prompt_type", "PLANNING").upper()
+        guidelines = cls.get_guidelines(prompt_type)
 
         prompt = cls.prompt_template
+        prompt = prompt.replace("{PROMPT_TYPE}", prompt_type)
+        prompt = prompt.replace("{GUIDELINES}", guidelines)
         prompt = prompt.replace("<curr_instructions>", input_dict["current_instruction_doc"])
         prompt = prompt.replace("<inputs_outputs_feedback>", format_samples(input_dict["dataset_with_feedback"]))
+
         return prompt
 
     @classmethod