Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair

Jesus Salas

cs.AIAug 18, 2026

Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair

Authors: Jesus Salas

Organizations: Microsoft

Abstract

Machine-verifiable workflows produce governance records linking a task contract, model attempt, verifier decision, accepted output, and target origin. We test whether verifier-admitted outputs can supervise a bounded model by consolidating occasional or expensive capability into reliable one-shot execution. On fresh, structure-disjoint PlanBench replanning cases, Qwen3-14B thinking produced 24 plans admitted by independently authored VAL. They trained the same checkpoint for non-thinking execution, without oracle targets or a stronger teacher. VAL acceptance rose from 1/80 to 57/80. A prospective replication held targets, model revision, recipe, and evaluation corpus fixed across eight LoRA seeds and three inference realizations per seed. Every seed produced a clear lift: adapters reached 45/80 to 70/80 against 1/80 for every matched base report; the exact seed-level sign-flip test gave p=0.0078125. Target-selection performance was less stable. An initial matched seed gave 102/160 accepted plans after VAL selection versus 69/160 after blinded model self-selection. Across eight prospective seeds, the contrast was seed-dependent, included one clear reverse seed, and did not replicate (p=0.3672). VAL also had a positive descriptive aggregate over schema-only selection but failed its preregistered seed-level reliability gate (p=0.0703). The verifier remains the admission authority; no reliable downstream capability advantage of semantic selection is established. A complementary Phi arm supports stronger-teacher distillation. Earlier synthetic studies bound teachability, cumulative learning, transfer, and stopping. The evidence supports robust consolidation of one fixed, machine-checkable capability, not arbitrary planning, enterprise validity, or unrestricted self-improvement.

Figures & tables

Path	Target producer	Training recipient	Target provenance	Frozen reading
Qwen	Qwen3-14B thinking	Same checkpoint, non-thinking	No oracle; no stronger teacher	Primary lift replicated
Phi	Phi-4-reasoning	Separate base Phi-4	No oracle; stronger teacher	Full-band distillation

Table 1: The two external paths share the governance-selected pipeline but not the same causal classification.

Figure 1: Fresh external outcomes on the shared 80-case PlanBench evaluation. The Qwen path is teacher-free same-checkpoint self-distillation. The Phi path uses a separate stronger reasoning teacher. Exact counts are printed above the bars.

Figure 2: Prospective replication of the primary teacher-free Self-24 intervention. Filled points are per-seed means and open points are three frozen inference realizations. The base result is 1/80 in every matched report. The training seed, not the repeated case, is the inferential unit.

Figure 3: Selector outcomes across eight prospective training seeds. The left panel is a descriptive pooled ladder. The right panel shows each seed’s mean paired net across three inference realizations; both sets of seed-level contrasts cross zero and neither passes its preregistered seed-level reliability test. The original base result on this 160-case corpus was 1/160, measured at one training seed and one inference realization; it is excluded from the common eight-seed ladder.

Figure 4: Verified performance versus mean request latency on the external evaluation. Arrows connect the expensive target-producing regime to the cheap trained specialist. The source-versus-specialist semantic comparisons are descriptive compression diagnostics, not preregistered superiority tests.

Construction and partition	Base	First adapter	Second adapter	Later adapter
Generator A, initial	10/48	21/48	n/a	n/a
Generator A, later	9/48	21/48	33/48	35/48
Generator B, Qwen	12/80	44/80	62/80	n/a
Generator C, Qwen	57/128	128/128	126/128	n/a
Generator B, Phi self-training	10/80	77/80	n/a	n/a

Table 2: Fresh synthetic one-response repair after training only on model- generated, verifier-selected targets. The two Generator A rows use different case-disjoint evaluation partitions. The later Generator A step does not clear its frozen materiality gate.

Figure 5: Qwen self-sourced one-response learning across three synthetic constructions. Generator C’s high base rate and first-round saturation limit its evidence about iteration.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Harvest measure	Observed	Frozen gate
Distinct VAL-admitted targets	24/32	at least 16
First-attempt successes	13/32	reported
Successes within eight attempts	24/32	reported
Beyond-first-attempt headroom	11	at least 8
Completed HTTP requests	118/118	at least 98%
Completion tokens	419,201	at most 2,500,000

Appendix

Table 3: Formal Qwen target acquisition on the fresh harvest partition.

Qwen arm	VAL accepted	Schema-valid	Mean latency	Completion tokens
Base, non-thinking	1/80	71/80	0.624 s	1,729
Base, thinking	30/80	77/80	65.964 s	254,960
Self-24, non-thinking	57/80	80/80	1.186 s	2,965

Appendix

Table 4: Fresh Qwen evaluation on the external 80-case partition. Token counts are totals across each arm.

Selector	VAL-valid targets	Train time	Final loss
VAL	24/24	64.1 s	0.3545
Qwen self-selection	20/24	64.2 s	0.3905
Schema-only frozen order	15/24	64.0 s	0.5077

Appendix

Table 5: Matched target sets and training outcomes. Only target selection changes across arms.

Evaluation arm	VAL accepted	Schema-valid	Mean latency
Base, non-thinking	1/160	129/160	1.395 s
Schema-selected adapter	55/160	152/160	1.410 s
Model-self-selected adapter	69/160	159/160	1.385 s
VAL-selected adapter	102/160	160/160	1.202 s

Appendix

Table 6: Fresh outcomes for the matched target-selection ablation.

Training seed	Adapter reports	Base reports	Mean net	Reading
2026113002	59, 61, 59	1, 1, 1	$+58.67$	clear lift
2026113003	57, 59, 57	1, 1, 1	$+56.67$	clear lift
2026113004	45, 47, 45	1, 1, 1	$+44.67$	clear lift
2026113005	51, 52, 51	1, 1, 1	$+50.33$	clear lift
2026113006	59, 58, 57	1, 1, 1	$+57.00$	clear lift
2026113007	65, 65, 66	1, 1, 1	$+64.33$	clear lift

Appendix

Table 7: Accepted plans out of 80 for the eight prospective Self-24 training seeds. Each report column contains the three frozen inference realizations.

Seed	Schema	Model self	VAL	VAL $-$ self	VAL $-$ schema
2026120305	102	123	190	$+67$	$+88$
2026120306	164	120	141	$+21$	$-23$
2026120307	129	248	288	$+40$	$+159$
2026120308	147	151	118	$-33$	$-29$
2026120309	110	197	226	$+29$	$+116$
2026120310	146	215	149	$-66$	$+3$

Appendix

Table 8: Prospective common-seed selector results. Totals are descriptive; exact tests use the eight training-seed effects rather than pooled cases.

Phi arm	VAL accepted	Schema-valid	Mean latency	Completion tokens
Base Phi-4	2/80	35/80	1.027 s	3,362
Phi-4-reasoning	30/80	41/80	150.283 s	544,408
Distilled Phi-4	51/80	80/80	0.981 s	2,531

Appendix

Table 9: Fresh Phi evaluation on the same external 80-case partition. Token counts are totals across each arm.

Stage	Model	Training-target source	Fresh evaluation	Evidentiary role
Oracle precursor	Phi-4, Qwen3-14B	Procedural schedule, verifier-confirmed	144 rows per model	Teachability and signal attribution
Generator A	Qwen3-14B	Same-model accepted samples	48-row one-response and 32-row search pools	Bounded loop, cumulative learning, stopping
Generator B	Qwen3-14B	Same-model accepted samples	80-row one-response and 32-row search pools	Native construction replication and transfer boundary
Generator C	Qwen3-14B	Same-model accepted samples	128-row one-response and 32-row search pools	Construction-separated first-round replication
Generator B	Phi-4	Same-model accepted samples	80-row one-response and 32-row search pools	Cross-model method replication

Appendix

Table 10: Synthetic evidence map. All evaluation partitions are case-disjoint from their training targets. Generator separation is by implementation, not by independent authorship.

Training state	Inference feedback	Pass	Transport	Compound
Base	Localized	31/144	128/144	5/48
Localized-trained	Localized	139/144	144/144	45/48
Base	Verdict-only	35/144	135/144	9/48
Localized-trained	Verdict-only	115/144	144/144	33/48
Verdict-only-trained	Localized	133/144	144/144	41/48
Verdict-only-trained	Verdict-only	136/144	144/144	44/48

Appendix

Table 11: Qwen training and inference feedback cross. The primary causal comparison fixes localized inference and changes only the training trace.

Search condition	Accepted	Pass@1	Later	Calls	Transport
Fixed generic	12/32	8/32	4	678	678/678
Fixed localized	16/32	10/32	6	591	590/591
Adaptive localized	16/32	11/32	5	539	539/539

Appendix

Table 12: Target-hidden search attribution. “Later” counts targets first accepted after at least one verifier rejection.

Construction and model	Earlier arm	Later arm	Paired reading
A, Qwen initial	Base 10/48	Self-16 21/48	14 gains, 3 losses, $p=0.012726$
A, Qwen Round 2	Self-16 25/48	Default30 35/48	Net $+10$ , $p=0.021271$
A, Qwen Round 3	Default30 33/48	Round3 35/48	3 gains, 1 loss, $p=0.625$ ; stop
B, Qwen Round 1	Base 12/80	B-Round1 44/80	Net $+32$ , $p=4.07\times 10^{-9}$
B, Qwen Round 2	B-Round1 44/80	B-Round2 62/80	Net $+18$ , $p=4.01\times 10^{-5}$
C, Qwen final	Base 57/128	C-Round2 126/128	69 gains, 0 losses, $p=3.388\times 10^{-21}$

Appendix

Table 13: Fresh one-response transitions from verifier-selected self-training. The Generator A rows use separate case-disjoint partitions. Generator C Round 1 reaches 128/128; its final Round 2 arm reaches 126/128 and does not improve on the saturated first-round measurement.

Figure 6: First-attempt and 32-attempt success on matched pools. Panels use different case sets and are not pooled. Training expands fixed-budget reachability through Default30 on Generator A and Round 1 on Generator B; Generator C saturates the frozen frontier after Round 1.

Figure 7: Zero-shot transfer from Generator A adapters to Generator B. Default30 passes the aggregate gate but crosses the frozen family-regression guard. Round3 is a predeclared secondary trajectory arm and cannot replace the primary treatment after results are opened.

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Test-time reasoning is increasingly used as a serving-time control knob, but extra reasoning is not uniformly valuable: it can repair failed attempts, waste compute on already-correct answers, or introduce harmful answer changes. We study this as a deployment allocation problem rather than a new-verifier problem. We introduce \sevra, Selective Verification for Reasoning Allocation, a serving-layer controller that decides whether to preserve a frozen solver's initial answer or invoke active verification. Using a frozen Qwen3-4B solver, we log intervention outcomes and train recoverability-aware gates from serving-visible attempt state. On \mathfive, selective verification reaches 76.3% accuracy, compared with 75.5% for always verifying, while reducing post-generation tokens by 26.8% and harmful flips from 2.2% to 1.0%. However, an 8,192-token initial solve reaches 76.0% accuracy with 28% fewer total model tokens, showing that selective recovery is useful but not the best tested cost frontier. In frozen transfer to \gsm, the selective policy verifies only 3.0% of examples, improves accuracy from 93.4% to 94.5%, and reduces verification tokens by 91.2% relative to always verifying; again, a longer initial solve matches its accuracy with fewer realized tokens. On CommonsenseQA, always-on verification hurts, while Self-Consistency@5 improves accuracy at about five times the realized token cost. The resulting deployment rule is: tune the initial budget first, then use selective recovery when explicit checks, bounded retries, auditability, or regression-risk control matter.

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