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Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

Xing Zhang, Guanghui Wang, Yanwei Cui, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He

arXiv:2610.11464v1cs.AIcs.CLcs.LG

TL;DR

Self-improving agents need reliable verification, but open-ended tasks often lack an accurate verifier and fall back on manual rubrics or model judges. This paper evolves an inspectable composition of drawback detectors using anchored agreement and unlabeled consensus rather than agent scores. The evolved verifier improves held-out agreement on MBPP+ and retains most of the reference loop’s skill gains across three task areas.

  • Problem

    Open-ended self-improving agents lack accurate verifiers, so systems rely on manually written rubrics or demonstrations to judge whether an attempt passed.

    Source: §1 · p. 1

  • Method

    The paper evolves an inspectable composition of mostly deterministic drawback detectors, selected by agreement with a ten-item anchor and consensus on unlabeled outputs rather than agent performance.

    Source: §1 · p. 2

  • Results

    88–110% of the lift from ground truth or a rubric is retained by Double Ratchet across code generation, enterprise text-to-SQL, and reference-free report generation.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Downstream skill performance cannot validate a self-evolved verifier, because a vacuous always-pass grader can train skills just as well as a valid one.

    Source: §5.2 · p. 7, §6 · p. 9

    A skill loop is a forgiving consumer of grades, and residual failures often require live exploration that guidance-text skills cannot express.

    Source: §5.3 · p. 8

Abstract

from arXiv · show

We changed the agent: did it actually get better? Every self-improving agent loop answers this hundreds of times, and every answer comes from a verifier. On open-ended tasks none exists, so the loop is handed a hand-written rubric or a bare LLM judge grading output from a model like itself, inviting reward hacking and shared blind spots. We make the verifier the evolving object: an inspectable expression over small, mostly deterministic drawback detectors, synthesized from clustered failures, gated at birth, and selected for agreement with a ten-item anchored reference set plus consensus over unlabeled outputs, never for the agent's score. On MBPP+ it gains +0.21 held-out agreement over the hand-authored seed composition, on every seed, and ends ahead of the bare LLM judge it contains. One finding should change how co-evolved verifiers are validated: removing the anchor guards collapses the verifier into a vacuous always-pass grader, yet that collapsed verifier trains skills just as well. Downstream task score cannot certify a self-evolved verifier. Score does answer sufficiency, and there an evolved verifier can substitute: Double Ratchet, pairing the verifier with a lifecycle-managed skill loop, retains 88-110% of the lift that ground truth or a rubric buys the same loop, across code generation, enterprise text-to-SQL, and reference-free report generation. When evolved skills gamed the report rubric, an outer judge caught it and one added detector repaired it; the judge itself was wrong until given the task contract.

1 Introduction

Self-improving agents need a verifier to tell whether changes help, but open-ended tasks lack accurate verifiers and rely on manual rubrics or demonstrations. This paper evolves an inspectable verifier using anchored agreement and unlabeled-output consensus, then tests its validity and usefulness to a skill loop.

1 Introduction

  • A self-improving loop needs a verifier each round, while deployed open-ended tasks lack one and instead use hand-written rubrics or workflow demonstrations.The paper frames verifier construction as a bottleneck beyond benchmarks with unit tests.Source: §1 · p. 1
  • The verifier—not the agent or task set—is the evolving object, with fixed task sets and references preventing results from coming from an easier test.The paper's target setting has no accurate verifier to beat, so success is measured by how much improvement an accurate-verifier reference loop recovers.Source: §1 · pp. 1–2
  • The verifier composes mostly deterministic, typed drawback detectors, making failures nameable and the grader inspectable and repairable.The detectors span static checks, execution, and narrow judge queries, rather than trusting a single scalar judge.Source: §1 · p. 2
  • Selection uses agreement with a ten-item anchored reference set and detector consensus on unlabeled outputs, never the agent’s score.A locked test set is reserved for verifier evaluation and skill-side rollback.Source: §1 · p. 2
  • The paper contributes verifier evolution, evidence that anchor discipline carries the safety load, and Double Ratchet, which co-evolves the verifier with a skill loop.It also reports a reward-hacking episode caught by an outer judge and repaired with an added detector; the judge needed the task contract.Source: §1 · p. 2

2 Related work

Prior agent-skill loops generally take their verification signal as given, while learned graders can be opaque and vulnerable to gaming. This work instead searches over composed detectors using anchored agreement and consensus without relying on downstream task scores.

2 Related work

  • Agent-skill systems evolve libraries or workflows, but the Ratchet loop [1] [22] is adopted unchanged here and is not a contribution of this work.The paper's distinct focus is evolving the verification signal those systems normally take as given.Source: §2 · p. 2
  • Prior searches over losses, learning algorithms, pruning metrics, and code score against supplied evaluators, whereas this regime lacks a valid metric and uses anchored agreement plus consensus.The paper contrasts its setting with approaches that assume machine-gradable progress.Source: §2 · pp. 2–3
  • Learned graders can be over-optimized or opaque; this verifier instead composes typed detectors to predict an unseen anchor and can be inspected and repaired by hand.CodeScore [45] and SQLHD [46] are treated as possible detector components, not competing full systems.Source: §2 · p. 3

3 The verifier and its evolution

The verifier is a composition of drawback detectors evolved through anchored supervision and unlabeled consensus. Double Ratchet then uses that verifier to grade skill training while keeping evaluation and rollback tied to a locked reference.

3 The verifier and its evolution

  • The split design uses unlabeled train outputs for verifier disagreement, ten anchored dev items as its only supervised signal, and a locked test set for out-of-sample validity and skill-side rollback.The verifier never reads the test reference; the reference and co-evolved skill loops use the same rollback procedure.Source: §3 · pp. 3–4

3.1 Verifiers as compositions of drawback detectors

  • A verifier composes typed drawback detectors that check one failure class, spanning static inspection, execution, and narrow LLM-judge queries.Logical combinations handle detector verdicts and abstentions; the fixed root checks basic structure, while reported selected expressions are reproducible from their expression string and detector pool.Source: §3.1 · p. 4

3.2 The verifier loop

  • The verifier loop clusters anchored misses and unlabeled gaps into detector proposals, gates new detectors, and selects expressions by recall-weighted anchor agreement multiplied by reliability-weighted consensus, with size penalized.Unanchored detectors enter as unselectable shadows until they raise dev agreement; the loop also retires detectors with non-positive marginal contribution and reports locked-test agreement only as measurement.Source: §3.2 · pp. 4–5

3.3 Co-evolution with the skill loop: Double Ratchet

  • Double Ratchet alternates verifier and skill phases, using the evolved verifier to grade training attempts while a locked test set remains the skill loop’s evaluation and rollback anchor.The comparison is an otherwise identical reference skill loop with a ground-truth verifier or best available rubric; a separate outer judge audits final outputs but is not a training signal.Source: §3.3 · p. 5

4 Experimental setup

The experiments validate verifier accuracy on code generation and enterprise text-to-SQL, then study report generation without a gold verifier. They use screened tasks, ten-item dev anchors, and single-call LLM solvers under fixed configurations.

4 Experimental setup

  • MBPP+ code synthesis and Spider 2.0-Snow text-to-SQL provide hard anchors, while report generation has only rubric scores and golden demonstrations as partial references.The report task generates analyst-report sections from audited evidence cards and has no golden verifier.Source: §4 · p. 5
  • MBPP+ and Spider tasks passed on every one of five frozen-solver samples are excluded, and dev sets contain five clear failures plus five clean positives.The solver is a single direct LLM call without tools, iteration, or scaffolding, so absolute scores are not comparable to leaderboard agents.Source: §4 · p. 5
  • All in-loop roles use Claude Opus 4.7, while report-output audits use Claude Opus 4.8 outside the loops; skill runs last 100 rounds.Co-evolution interleaves verifier phases within four 25-round skill phases, matching the reference loop’s skill budget.Source: §4 · p. 6

5 Results

The evolved verifier improves held-out agreement over its hand-authored starting point, while ablations show that anchor guards—not downstream task scores—are essential for validating it. Double Ratchet retains most of the lift available from ground-truth or rubric feedback, while report-generation audits expose both reward hacking and the need for a task-aware judge.

5.1 The evolved verifier beats the engineering it starts from

  • Gap-driven synthesis and birth gating leave a working pool of a dozen to a few dozen detectors, from which anchored selection keeps one to three leaves.Replay with four-, six-, and eight-item report anchors did not lower mean held-out agreement, but widened its spread.Source: §5.1 · pp. 6–7
  • +0.21 held-out agreement on MBPP+ lifts the evolved composition from 0.417±0.058 for the hand-authored seed to 0.625±0.050, ahead of the contained bare LLM judge at 0.55 ± 0.04.Every seed is net positive, though task-clustered paired tests do not establish significance.Source: §5.1 · p. 6, Figure 2 · p. 7

5.2 Which guard carries the load, and why task score cannot tell

  • On report generation, disabling anchor guards collapsed the verifier into a fail-open grader that passed 0.97–1.00 of outputs, although task scores did not separate it from the other arms.The selection objective can rise while locked-set transfer falls: five of nine runs ended below their mid-run peak.Source: §5.1 · p. 6, Figure 3 · p. 7, §5.2 · p. 7

5.3 Sufficiency: an evolved verifier substitutes for ground truth

  • Double Ratchet retained 106%, 110%, and 88% of the reference loop’s lift on MBPP+, Spider, and Report, respectively.The ground-truth-task differences fall within noise; Report has a small gap against a reference that is itself a partial rubric.Source: §5.3 · p. 8
  • Spider’s verifier reached only 0.500 ± 0.026 agreement with ground truth while its co-loop retained the full reference lift.The verifier acts directionally by deciding which attempts become failure capsules; concrete error text does not depend on a correct pass/fail label. Both loops’ smaller Spider lift reflects residual failures needing live exploration that guidance-text skills do not express.Source: §5.3 · p. 8

5.4 A reward-hacking episode, and auditing the auditor

  • Report skills gamed the rubric by substituting evidence tags for values, leaving about 30% of tags valueless; a coupled detector-and-hint repair cut erased tags to about 1%.The rerun’s rubric score rose to 0.850 ± 0.010, but the study does not isolate the detector from the rewritten hints.Source: §5.4 · pp. 8–9
  • The generic judge barely moved its win rate from 0.122 to 0.126 after repair, while the task-aware judge moved from 0.515 to 0.770.The task-aware rubric was written after reading the generic judge’s rationales, so it diagnoses the audit rather than independently confirming the repair.Source: Table 2 · p. 9, §5.4 · p. 9

5.5 The detectability spectrum

  • Verifier agreement tracks failure detectability: Spider 2.0 reached 0.85 on compile-error-heavy failures, then 0.500±0.026 when schema grounding left semantically wrong values invisible to deterministic detectors.That setting shifts more of the burden to judge operations and human audits.Source: §5.5 · p. 9

6 Discussion and conclusion

The verifier evolves coverage of known drawbacks without creating ground truth, and the paper argues that safety depends on anchors, legibility, and independent auditing. Its evidence is bounded to a mechanism study rather than a scaling result.

6 Discussion and conclusion

  • Evolution expands drawback coverage but does not create ground truth; without an anchor, the loop optimizes noise.The authors identify anchor quality and hardening soft anchors into checkable ones as important.Source: §6 · p. 9
  • The study covers three task families, one model family, low-hundreds rounds, and held-out sets of tens of items, not scaling.The outer audit uses one stronger model rather than a panel or human study.Source: §6 · p. 9
  • A self-evolved verifier must not be validated by downstream agent performance, because an always-pass grader can pass that test too.The paper instead calls for an anchor the verifier predicts but never sees, plus a judge outside every loop.Source: §6 · p. 9

A Task descriptions

The evaluation spans code generation, enterprise text-to-SQL, and report generation, with different available anchors. Report quality has no accurate verifier, so its rubric serves only as reference signal.

A Task descriptions

  • MBPP+ tests Python functions against hidden unit tests, with typical failures including crashes, edge cases, and misread specifications.Its EvalPlus suite adds tests to the original MBPP and repairs faulty ones.Source: A Task descriptions · p. 13
  • Spider 2.0-Snow tests SQL against a live Snowflake warehouse, comparing fetched results with stored gold results using the official value-based comparator.Hard failures include cleanly executing queries that compute the wrong result.Source: A Task descriptions · p. 13
  • Report generation asks for an evidence-grounded section with tagged key figures, calibrated claims, and required structure, but has no accurate verifier.Its rubric scores lexical and structural proxies and is treated only as reference signal.Source: A Task descriptions · p. 13

B Setup details

The setup screens and splits tasks for evaluation, uses a ten-item dev set, and defines task-specific anchors. Spider prompting applies the same schema grounding across arms to control for its shared effect.

B Setup details

  • The screened train/dev/test sets contain 60/10/40 MBPP+ tasks, 59/10/40 Spider tasks, and 73/10/48 report sections.Code and SQL tasks are screened with five frozen-solver samples, always-passed tasks are dropped, and the remainder are stratified before a 60/40 train-test split.Source: B Setup details · p. 13
  • Each dev set has ten items, balancing five clear failures with five clean positives or golden-quality positives.For reports, the five failures are class-stratified and checkable.Source: B Setup details · p. 14
  • Anchors are hidden tests for MBPP+, execution match for Spider, and frozen demo-delta soft labels for report generation.The report loop's 96 locked items make an always-pass verifier score 0.56; skill-loop scoring uses the official 0–100 normalized rubric.Source: B Setup details · p. 14
  • Spider schema grounding raises the frozen baseline from 0.211 to 0.329, but is applied identically across arms and is not a contribution.Grounding also shifts residual failures from compile errors toward clean-executing wrong values.Source: B Setup details · p. 14

C Expression grammar, selection, and lifecycle constants

Verifier expressions combine typed drawback detectors, with selection balancing anchored agreement against unlabeled consensus and lifecycle gates. The final-judge protocol compares stored outputs under generic and task-aware rubrics.

C Expression grammar, selection, and lifecycle constants

  • The verifier grammar combines detector leaves using disjunction, conjunction, negation, K-of-k votes, or weighted votes against a threshold.Expression verdicts distinguish DRAWBACK from ABSTAIN.Source: C Expression grammar, selection, and… · p. 14
  • Selection multiplies recall-weighted dev agreement by reliability-weighted train consensus, then subtracts a complexity penalty.Candidates without usable dev opinion and all-pass, all-fail, or all-abstain candidates are rejected; new detectors must pass birth gates, and non-positive detectors retire after a grace period.Source: C Expression grammar, selection, and… · p. 14

D Final-judge protocol and rubrics

  • The final-judge protocol pairs stored round-0 reports with peak- and final-round reports, using two position-swapped judgments and counting only consistent wins.The generic rubric asks about holistic reader value; the task-aware rubric states the report format contract.Source: D Final-judge protocol and rubrics · pp. 14–15
  • The task-aware rubric treats non-rendering output, valueless metric tags, and fabricated figures as defects, with 271 of 526 pre-repair and 435 of 565 post-repair decided pairs.Table 2's four cells vary only by repair pass and rubric.Source: D Final-judge protocol and rubrics · p. 15

E What evolution actually produced

The evolved verifiers take task-specific forms, combining drawback detectors into explicit grading rules. Their lifecycle can add a detector only after it improves dev agreement, while their named failure classes can inform skill repair.

E What evolution actually produced

  • The final MBPP+ verifier flags specification mismatches, crashes on probes, or printing instead of returning; Spider 2.0 and report verifiers use task-specific detector combinations.On Spider 2.0, the form was independently selected on two of three seeds; report seeds vary one detector.Source: E What evolution actually produced · p. 15
  • A synthesized Spider detector entered as a shadow and was adopted only after composition raised dev agreement from 0.71 to 0.81.It replaced the seed pool’s grouping check; other seeds converged on different leaves.Source: E What evolution actually produced · p. 15
  • On the ratio task, the evolved verifier flags a specification mismatch that hidden tests report only as FAIL, naming the failure class for skill synthesis.The other checks—crash and returns_not_print_only—are clean.Source: E What evolution actually produced · p. 15
  • Independently synthesized skills across MBPP+, Spider 2.0, and Report express similar task-specific repairs under reference and evolved-verifier grading.Examples include copying function names from assertions, diagnosing empty SQL results, and placing metric tags after numbers.Source: E What evolution actually produced · p. 15

F Lifecycle statistics and full per-loop scores

This section reports held-out learning-curve comparisons, verifier-validity ablations, lifecycle and skill-bank behavior, and limits on what the loops can establish. It also examines anchor size and cases where weak verifiers still accompany strong skill-loop scores.

F Lifecycle statistics and full per-loop scores

  • Removing anchor guards collapses the verifier into an always-pass grader on every seed, while removing the detector lifecycle does not; held-out task score misses the collapse.Table 3 reports final-round values over three seeds, with each arm flipping one guard family.Source: Table 3 · p. 16
  • The skill loop synthesizes roughly 1.5 skills per round, evicts more than half of those created, and rarely rolls back.The report loop is an outlier: one seed maintains its plateau with a bank of six skills.Source: F Lifecycle statistics and full… · p. 16
  • Figure 4 compares held-out reference-loop and Double Ratchet curves; frozen single-call baselines are approximately 0.27 on MBPP+, 0.285 on Spider 2.0, and 0.56 on report generation.The figure’s callout expresses co-evolution’s lift retention as a fraction of the reference loop’s.Source: F Lifecycle statistics and full… · p. 15, Figure 4 · p. 16
  • Anchored selection favors precise, small detector compositions over detector accumulation across the 100-round lifecycle.Table 4 reports three-seed min–max values; a bare number means all seeds agree.Source: Table 4 · p. 16
  • Guidance-text skills cannot substitute for interaction-dependent capabilities, the ceiling both loops reach on Spider 2.0; no selected verifier used the implemented failure-anticipating detectors.The paper makes no claim about those detectors.Source: F Lifecycle statistics and full… · p. 16
  • Naive co-loops match the anchored arm on held-out evaluation despite degenerate verifiers on every task; their objective barely moves on two of three Spider 2.0 seeds.The measurement anchor remains untouched in these co-loops.Source: F Lifecycle statistics and full… · p. 16
  • Two of three Spider co-evolution seeds selected weak single-detector verifiers with train agreement 0.41–0.44 yet peaked at 0.45–0.50, indistinguishable from the sibling seed.The sibling seed used a two-detector verifier.Source: F Lifecycle statistics and full… · p. 16
  • Held-out agreement was 0.871, 0.882, and 0.874 with four, six, and eight anchor items, respectively, versus 0.854 with ten.Smaller anchors increased subsample variability: standard deviation was 0.063 at four items versus 0.041 at eight.Source: F Lifecycle statistics and full… · p. 17

G Reproducibility

The experiments use hosted model APIs and a CPU host, with run histories and verdict traces stored for recomputation. Model assignments are configured per role, while Spider 2.0 requires credentialed warehouse access.

G Reproducibility

  • All model calls use hosted APIs, so runs need a CPU host and wall-clock time is dominated by API latency.The reported host had 48 CPU cores and 186 GB RAM.Source: G Reproducibility · p. 17
  • Runs store round-by-round histories, detector pools, skill banks, and judge outputs in JSON and SQLite, including 52,600 locked verdicts with per-detector traces.Reported numbers are read from these files, enabling paired tests to be recomputed without rerunning loops.Source: G Reproducibility · p. 17
  • Each LLM role is bound per call from a role-to-model table; Spider 2.0 requires credentialed access to its hosted warehouse.The experiments hold model assignments uniform; other experiments are self-contained.Source: G Reproducibility · p. 17
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