rlm-gepa

name	rlm-gepa
description	Add, evaluate, and optimize RLM-GEPA projects for an existing PredictRLM. Use when a user wants to improve reusable RLM instructions from execution traces, build a train/validation optimization harness, configure AgentSpec or OptimizeConfig, run optimize --check, or inspect RLM-GEPA run artifacts. For creating the base RLM itself, use rlm first.
compatibility	Requires Python 3.11+, Deno 2, predict-rlm[gepa] 0.8.0-alpha0 or newer, and a working PredictRLM.
metadata	{"author":"Emile Riberdy","version":"1.0"}

Optimize an RLM with RLM-GEPA

RLM-GEPA improves reusable text components, usually a Skill.instructions string, using scored executions of a real PredictRLM. It is not a replacement for initial RLM design. Start only after the base RLM has a concrete signature, tools, a measurable outcome, and a working smoke path.

The system has two loops:

1. Executor loop — run each candidate RLM against train or validation examples, collecting outputs, RunTrace objects, scores, and feedback.
2. Proposer loop — use scored traces to make a surgical edit to a declared mutable component.

AgentSpec controls which transferable behaviors the proposer may improve. OptimizeConfig controls budget, concurrency, models, and search behavior. Keep those concerns separate.

Workflow

Step 1: Confirm optimization fit

Use RLM-GEPA when the user wants a reusable behavior to improve and can provide:

• a working PredictRLM with its real DSPy signature and tool surface;
• a text component worth optimizing, such as domain skill instructions;
• representative examples and a deterministic or carefully designed score;
• concrete feedback for imperfect outputs.

Do not optimize a vague prompt before the base RLM works. First fix functional bugs, missing tools, weak output schemas, and unsupported sandbox dependencies. Do not use GEPA for a one-off answer or a generic chat-agent prompt.

Step 2: Audit data and split boundaries

Inspect examples before coding loaders or split logic. Identify input shapes, reference-output shape, duplicates, shared source groups, missing labels, and failure modes the score should expose.

Use consistent semantics:

• Train — examples available to proposer and candidate-gating work.
• Validation — examples used to select candidates and catch regressions.
• Held-out test — optional reporting-only data. Do not leak it into prompt text, proposer traces, candidate selection, or optimization feedback.

Prefer deterministic splits. Record the seed, grouping key, counts, and sampling limits. Split by document, user, task family, or source whenever related rows would otherwise leak across train and validation.

Keep the evaluator harness-side. The RLM may use safe task tools and documents, but it must not see hidden answer keys, oracle APIs, or evaluator feedback while solving an example.

Step 3: Define the mutable component and AgentSpec

Build the concrete RLM first. Derive its signature and tools with agent_spec_from_rlm(...); do not duplicate those broad descriptions manually. Add only context that the RLM cannot reveal:

• at least two distinct use_cases defining the transfer boundary;
• at least three nonempty runtime_grounding_examples groups containing stable runtime facts;
• scoring_description explaining score, partial credit, and hard failures;
• optional counterfactual_axis_name (domains, task shapes, failure modes, task types, or problem classes) and an anti-hack domain_conventions_note.

A useful AgentSpec teaches grounded behavior, not benchmark trivia. Name actual tool contracts, library symbols, sandbox facts, and evaluator-visible failure surfaces. Do not key instructions on task IDs, file names, row counts, or answer artifacts.

from predict_rlm import PredictRLM, Skill
from rlm_gepa import agent_spec_from_rlm


SEED_INSTRUCTIONS = "Inspect evidence before producing a grounded result."


def build_rlm(skill_instructions: str, *, lm=None, sub_lm=None, max_iterations: int = 30):
    return PredictRLM(
        AnalyzeDocuments,
        lm=lm,
        sub_lm=sub_lm,
        max_iterations=max_iterations,
        verbose=False,
        skills=[Skill(name="document-analysis", instructions=skill_instructions)],
    )


agent_spec = agent_spec_from_rlm(
    build_rlm(SEED_INSTRUCTIONS),
    use_cases=[
        "contract review with clause-level citations",
        "invoice analysis with total reconciliation",
    ],
    runtime_grounding_examples={
        "skills": ["document-analysis instructions are the mutable component"],
        "sandbox facts": ["Pyodide packages must be compatible with WASM"],
        "document behaviors": ["tables may span pages", "OCR text can be missing"],
    },
    scoring_description="Score combines correctness and citation support; feedback names missing or unsupported findings.",
    counterfactual_axis_name="task shapes",
    domain_conventions_note=(
        "Rules must transfer to unseen files; do not key behavior on file names, "
        "task IDs, or reference-answer artifacts."
    ),
)

Step 4: Implement the project contract

Subclass RLMGepaProject. Its contract is intentionally small:

• seed_candidate() returns exactly the declared component keys and nonempty seed text.
• load_trainset() and load_valset() return nonempty sequences.
• evaluate_example() runs the candidate RLM, scores it, and returns a finite RLMGepaExampleResult with feedback, traces, stable example_id, and useful rlm_inputs.

from dataclasses import dataclass
from typing import Any

from predict_rlm.trace import RunTrace
from rlm_gepa import EvaluationContext, RLMGepaExampleResult, RLMGepaProject
from .bench import load_examples, score_result



@dataclass
class EvalExample:
    example_id: str
    rlm_kwargs: dict[str, Any]
    reference: Any


class DocumentProject(RLMGepaProject):
    project_name = "document-analysis"
    components = ("skill_instructions",)
    agent_spec = agent_spec

    def seed_candidate(self) -> dict[str, str]:
        return {"skill_instructions": SEED_INSTRUCTIONS}

    def load_trainset(self) -> list[EvalExample]:
        return load_examples("train")

    def load_valset(self) -> list[EvalExample]:
        return load_examples("validation")

    async def evaluate_example(
        self,
        candidate: [, ],
        example: EvalExample,
        context: EvaluationContext,
    ) -> RLMGepaExampleResult:
        rlm = build_rlm(
            candidate[],
            lm=context.lm,
            sub_lm=context.sub_lm,
            max_iterations=context.max_iterations,
        )
        result =  rlm.acall(**example.rlm_kwargs)
        score, feedback = score_result(result, example.reference)
        trace: RunTrace |  = (result, , )
         RLMGepaExampleResult(
            score=score,
            feedback=feedback,
            traces=[trace]  trace     [],
            rlm_inputs={: example.example_id, **example.rlm_kwargs},
            example_id=example.example_id,
        )

For an imperfect result, feedback must be nonempty. Good feedback identifies the actual failing surface: page, cell, clause, tool response, assertion, timeout, or schema mismatch. It describes what happened, not replacement prompt text.

Override task_timeout_for_example(example, default_timeout) or task_resources_for_example(example) only when the workload genuinely has per-example timeout or resource needs. Override minibatch_group_id(example) when grouped examples must be sampled together.

Step 5: Configure the project CLI

Keep optimization code local to the generated project:

my_rlm/
├── agent/             # Existing PredictRLM signature, service, skills, tools
├── bench/             # Examples, split policy, deterministic scorer
└── gepa/
    ├── __init__.py
    ├── config.py      # OptimizeConfig defaults
    ├── project.py     # RLMGepaProject implementation
    └── cli.py         # run_project_cli wiring

Install the required extra and expose the project CLI:

[project]
dependencies = [
    "predict-rlm[gepa]>=0.8.0a0,<0.9",
]

[project.scripts]
rlm-gepa = "my_rlm.gepa.cli:main"

from rlm_gepa import OptimizeConfig, run_project_cli

from .project import DocumentProject


def default_config() -> OptimizeConfig:
    return OptimizeConfig(
        max_metric_calls=1000,
        minibatch_size=25,
        concurrency=8,
        max_iterations=30,
    )


def build_project() -> DocumentProject:
    return DocumentProject()


def main() -> int:
    return run_project_cli(build_project, default_config())

Set model, budget, concurrency, timeout, selection, merge, and telemetry choices in OptimizeConfig or explicit CLI flags. Do not hide experiment behavior behind environment-only switches.

Step 6: Verify before spending a budget

First validate the project without a real search:

uv run rlm-gepa optimize --check

Then use a small budget to validate the full loop. Only increase max_metric_calls, minibatch_size, or concurrency after the trace, score, and cost surfaces are trustworthy.

Useful commands:

# Project-specific eval surface, when the project implements it.
uv run rlm-gepa eval --dataset validation --limit 5

# Optimize after --check succeeds.
uv run rlm-gepa optimize --max-metric-calls 1000 --minibatch-size 25 --concurrency 8

# Inspect candidate quality and run cost.
uv run rlm-gepa stats runs/<run-dir> --format markdown
uv run rlm-gepa plot runs/<run-dir>

plot additionally requires the gepa-viz extra: predict-rlm[gepa,gepa-viz].

Optimization writes run_metadata.json, gepa_state.bin, optimization_summary.json, all_candidates.json, task and proposer traces, and cost_log.jsonl beneath the run directory. Treat those artifacts as the evidence for accepting a candidate, not a single aggregate score.

Guardrails

• Optimize only declared text components; do not let GEPA alter code, evaluator logic, data splits, or hidden answer access.
• Keep task execution and scoring deterministic where possible.
• Use the shared RLM-GEPA adapter semantics for concurrent evaluation rather than creating ad hoc task loops; it owns concurrency, timeouts, traces, progress, resources, and cost accounting.
• Use --verbose-rlm for human-readable RLM rollout traces and --debug-rlm for lifecycle diagnostics.
• Keep train evidence, validation selection, and held-out reporting separate.
• Treat patch merge as evidence-backed behavioral grafting from trace disagreement, never prompt concatenation or broad source-text import.