Meta Harness Retool

Meta-Harness (ReTool) — Harness Evolution

Run ONE iteration of harness evolution. Do all work in the main session. Do not delegate to subagents.

You do not run benchmarks. The outer loop (meta_harness_retool.py) runs eval after you write candidate harnesses.

Critical Constraints

• Produce exactly the candidate harness.py files requested in the task prompt.
• Each file must define class CandidateEnv(ReToolEnv).
• Each candidate should test one mechanism. Do not bundle unrelated ideas.
• Do not stop early or claim the current harness is optimal.

Anti-Overfitting Rules

• Do not hardcode answers, entities, numbers from specific questions, dataset names, or branches on question text.
• Do not specialize logic to AIME, DAPO, MATH, or any named split.
• General policies are allowed: e.g. "verify arithmetic with Python", "retry after interpreter errors", or "force a boxed final answer".

Context

You are evolving a harness for ReTool-style math solving. The model sees math problems, may call a Python tool named code_interpreter, receives stdout or Python errors, then must provide the final answer in \boxed{...} format.

Read these files for ground truth:

• environments/retool/base_harness.py — baseline ReToolEnv, default code_interpreter, default prompt, default env_response.
• environments/retool/env.py — reward, dataset loading, candidate schema validation.
• verl-recipe/retool/sandbox_fusion_tool_config.yaml — tool schema used by verl GRPO training.

Schema Contract

Your tool contract must match the GRPO yaml:

async def code_interpreter(code: str, timeout: int = 30, language: str = "python", memory_limit_mb: int = 1024) -> str:
    ...

The first parameter must be named code. If you redefine code_interpreter with a different first parameter, meta-harness eval may still generate a schema from your Python signature, but verl training will send {"code": ...} and tool execution will fail.

The baseline tool mirrors verl-recipe/retool/retool.py:

• extracts the first ```python fenced block when present
• wraps the last non-empty non-print line in print(...)
• sends a SandboxFusion-compatible HTTP request
• returns stdout plus stderr, or "no stdout here" on non-finished execution

Copy the baseline implementation before editing. Do not write a new sandbox client from memory.

Useful Mechanism Axes

• A: SYSTEM_PROMPT structure, e.g. explicit plan-code-check-answer ritual.
• B: USER_PROMPT_TEMPLATE structure, e.g. preserving the math problem while changing the tool-use instruction.
• C: code normalization, e.g. better extraction or safer print wrapping.
• D: interpreter feedback, e.g. structured error explanations or retry nudges.
• E: env_response / no-tool-call recovery logic, e.g. force at least one calculation for arithmetic-heavy problems without question-specific rules.
• F: turn budget and stop behavior via MAX_TURNS and stop hooks.

Bad candidates only change constants, punctuation, or a single prompt word. If the diff is only a parameter tweak, rewrite it as a real mechanism.

Reward Semantics

The meta-harness reward uses the last assistant message only and calls verl.utils.reward_score.math_dapo.compute_score(..., strict_box_verify=True). Correctness is treated as binary for selection:

• correct boxed final answer: 1.0
• missing malformed boxed answer or wrong answer: 0.0

format_reward is a logging metric only. It does not replace correctness.

Files To Read Before Proposing

1. logs/accepted_harness.txt
2. harnesses/<accepted>/harness.py
3. logs/frontier_val.json
4. logs/evolution_summary.jsonl
5. latest logs/iteration_*/comparison.json and report.md if present
6. logs/<profile>/<accepted>/<model>/val.json
7. logs/<profile>/<accepted>/<model>/trajectories.jsonl

Use failed trajectories to identify concrete failure modes: no tool call when calculation was needed, malformed tool arguments, Python error not recovered, unprinted value, overlong code, final answer outside \boxed{}, or numeric answer not verified.

Workflow

Step 0: Post-Eval Reports

For each logs/iteration_*/ with comparison.json but no report.md, write a short report covering accepted candidate, axes explored, and one next takeaway.

Step 1: Analyze

Read the accepted harness and recent trajectories. For each requested slot, form one falsifiable hypothesis tied to a failure mechanism you observed.

Step 2: Prototype

Prototype new code parsing, tool feedback, or env_response behavior before writing final harnesses. Use /tmp/ scripts and real failure snippets from trajectories. Prompt-only candidates may skip this.

Step 3: Implement

For each slot:

1. Copy harnesses/<accepted>/harness.py to harnesses/<name>/harness.py.
2. Make targeted changes only.
3. Validate syntax:

python -c "import ast; ast.parse(open('harnesses/<name>/harness.py').read()); print('OK')"

4. Re-read the file and confirm class CandidateEnv(ReToolEnv) exists.

Step 4: Self-Critique

Check:

• Does this introduce a new mechanism rather than a constant tweak?
• Does code_interpreter still accept code as the first parameter?
• Does the harness avoid dataset names and question-specific branching?
• Does the final-answer instruction still require \boxed{...}?
• If overriding env_response, does it return tool messages compatible with the baseline ReToolEnv.env_response shape?

Step 5: Write pending_eval.json

Write to the working-directory root:

{
  "candidates": [
    {
      "name": "<slot_name>",
      "hypothesis": "<one falsifiable claim>",
      "changes": "<specific implementation summary>",
      "axis": "<A|B|C|D|E|F>",
      "components": ["<short-tag-1>", "<short-tag-2>"]
    }
  ]
}

Output line at the end:

CANDIDATES: <name_1>, <name_2>, ...

Candidate File Skeleton

from environments.retool.base_harness import ReToolEnv, code_interpreter

SYSTEM_PROMPT = "..."
USER_PROMPT_TEMPLATE = "{question}\nThe answer format must be: \\boxed{'The final answer goes here.'}"


class CandidateEnv(ReToolEnv):
    SYSTEM_PROMPT = SYSTEM_PROMPT
    USER_PROMPT_TEMPLATE = USER_PROMPT_TEMPLATE
    TOOLS = [code_interpreter]

    # Optional: override env_response / stop hooks / get_prompt_messages.