Certified Long-Horizon Code Agent Evolution via Validation-Gated Skill Optimization

Yifan Wang; Hao Cheng; Xiaomin Li; Yuexing Hao; Hemanth Neelgund Ramesh; Dongwon Jung; Hao Tang; Keru Wang

cs.AISep 26, 2026

Certified Long-Horizon Code Agent Evolution via Validation-Gated Skill Optimization

Authors: Yifan Wang, Hao Cheng, Xiaomin Li, Yuexing Hao, Hemanth Neelgund Ramesh, Dongwon Jung, Hao Tang, Keru Wang,

Organizations: Purdue University · Microsoft · University of Washington · University of California, Davis ·

Abstract

Long horizon agent self-evolution without model weight updates is essential for enabling deployed agents to accumulate reusable skills and improve over time. Prior self-evolution work has focused primarily on short-horizon tasks, while repository-level software engineering remains unexplored despite being an ideal testbed for long-horizon adaptation. In this setting, agents are required to solve streams of sequential tasks, navigate complex dependencies with evolving repositories and persistently store and reuse experience. Text-based skill optimization offers an efficient, non-parametric approach for such adaptation. However, existing methods often suffer from unstable updates, performance drawdown, and agent collapse over extended deployments. In this paper, we formalize the concept of in-context self-evolution and introduce VALVE, a validated-gated framework for long-horizon skill optimization. We establish finite convergence, provide theoretical guarantees for future-task gain and drawdown, and derive the validation and evaluation holdout sizes required for a prescribed tolerance, with leading-order scaling Empirically, our pipeline, VALVE achieves stable self-improvement over evolution horizon spanning more than 1,000 SWE tasks, with average final and peak gains of $14.9$ and $16.5$ points across three frontier models (GPT-5.5, Claude-4.6 and MiniMax-M2.7). The validation gate reduces average drawdown by 75% and produces an 11x more compact skill bank than ungated evolution. We further present extensive ablations identifying the design choices most critical to long-horizon skill evolution.

Figures & tables

Panel of Figure 1 — Figure 1: Validation stabilizes long-horizon self-evolution. (a) Avg@3 resolved rate over approximately $1{,}000$ SWE-smith task arrivals. Both runs improve early, but the ungated run falls from a $91.7\%$ peak to $80.0\%$ , while the validated run peaks at $97.0\%$ and ends at $92.3\%$ . (b) Across all twelve model–repository runs, validation reduces average drawdown by $75\%$ .

MiniMax-M2.7
	Final resolved rate (avg@3, %)
Setting	pygments	sqlfluff	deepdiff	pydicom	Model Avg.
Base	$52.5$	$66.5$	$56.3$	$60.3$	$58.9$
SkillOpt	$60.0\ {\color[rgb]{0.1758,0.4883,0.332}(+7.5)}$	$66.0\ {\color[rgb]{0.668,0.2734,0.2734}(-0.5)}$	$\mathbf{69.0}\ {\color[rgb]{0.1758,0.4883,0.332}\mathbf{(+12.7)}}$	$60.7\ {\color[rgb]{0.1758,0.4883,0.332}(+0.4)}$	$63.9\ {\color[rgb]{0.1758,0.4883,0.332}(+5.0)}$
VALVE w/o val	$53.3\ {\color[rgb]{0.1758,0.4883,0.332}(+0.8)}$	$66.7\ {\color[rgb]{0.1758,0.4883,0.332}(+0.2)}$	$61.7\ {\color[rgb]{0.1758,0.4883,0.332}(+5.4)}$	$58.7\ {\color[rgb]{0.668,0.2734,0.2734}(-1.6)}$	$60.1\ {\color[rgb]{0.1758,0.4883,0.332}(+1.2)}$
VALVE	$\mathbf{61.0}\ {\color[rgb]{0.1758,0.4883,0.332}\mathbf{(+8.5)}}$	$\mathbf{72.7}\ {\color[rgb]{0.1758,0.4883,0.332}\mathbf{(+6.2)}}$	$65.3\ {\color[rgb]{0.1758,0.4883,0.332}(+9.0)}$	$\mathbf{61.7}\ {\color[rgb]{0.1758,0.4883,0.332}\mathbf{(+1.4)}}$	$\mathbf{65.2}\ {\color[rgb]{0.1758,0.4883,0.332}\mathbf{(+6.3)}}$

Table 1: Final performance after long-horizon SWE-smith repository-level evolution. Parentheses show gains over the corresponding base model; best results are bold.

MiniMax-M2.7
Setting	Final gain	Peak gain	Draw- down
SkillOpt	$+5.0$	$+7.4$	$\mathbf{2.3}$
VALVE w/o val	$+1.2$	$+5.4$	$4.2$
VALVE	$\mathbf{+6.3}$	$\mathbf{+9.0}$	$2.7$
GPT-5.5
SkillOpt	$+6.2$	$+12.8$	$6.6$

Table 2: Gain retention over evolution. Best results are bold.

Figure 3: Ablation of component-level design choices on pydicom with GPT-5.5. Appendix C.1 maps prior self-evolving methods to the compared alternatives; the step-0 baseline is $38.2\%$ .

Figure 4: Final and peak avg@3 resolved rate versus validation sizes using Claude-4.6 on repository pygments .

MiniMax-M2.7
	Final bank size (skills / principles)
Setting	pygments	sqlfluff	deepdiff	pydicom	Total
VALVE w/o val	$244/231$	$237/220$	$238/239$	$205/197$	$924/887$
VALVE	$30/34$	$38/38$	$14/13$	$34/31$	$116/116$
GPT-5.5
VALVE w/o val	$249/194$	$234/160$	$267/210$	$174/127$	$924/691$

Table 3: Final bank size after repository-level evolution. Entries report skills / principles.

Figure 5: Accumulated bank size over evolution for Claude-4.6 on pygments . Solid curves show avg@3 resolved rate, dashed curves show bank size, and triangles mark bank updates.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 7 — Figure 7: Holdout size required for a prescribed tolerance, from Corollary 1 . The vertical axes are logarithmic; horizontal lines mark the $100$ -task evaluation set and the $50$ -task validation panel used in our runs.

	Evaluation set $n_{\mathcal{H}}$			Validation panel $n$
Tolerance $\gamma$	$A=4$	$A=8$	$A=16$	$K\leq 3$	$K\leq 5$	$K\leq 7$
$2$ pp	$680$	$787$	$895$	$235$	$324$	$396$
$3$ pp	$328$	$380$	$432$	$195$	$268$	$327$
$5$ pp	$136$	$157$	$179$	$142$	$195$	$239$
$8$ pp	$62$	$72$	$82$	$99$	$136$	$166$
$10$ pp	$44$	$51$	$58$	$81$	$111$	$136$

Appendix

Table 4: Holdout size required for a prescribed tolerance, at

\delta=0.05

and

\sigma^{2}=\sigma^{2}_{\mathcal{H}}=0.025

. Left: tasks needed so that the certified population drawdown exceeds the measured one by at most

\gamma

, for

A

distinct banks among the checkpoints. Right: tasks needed so that every accepted update of a run making at most

K

of them costs no more than

\gamma

, at a gate-query horizon

T=60

and anchored at the observed accepted panel gain of

5.8

points.

Component	Reference setting
Data	One repository-level task stream from the SWE-smith training split
Evaluation set	100 tasks sampled without replacement (seed 42)
Validation panel	50 tasks sampled from the remaining stream (seed 123); the same fixed panel is used for every acceptance decision
Agent	Frozen code-agent backbone (MiniMax-M2.7, GPT-5.5, or Claude-4.6-sonnet, according to the run) with an interactive shell environment
Adaptation	3 independently sampled rollouts per task
Evolution trigger	Mixed-outcome tasks only; at most 100 mixed tasks

Appendix

Table 5: Reference configuration shared by the main repository-level runs.

Ablation	Method-level intervention
Sentence skills	Replace structured rich bodies with one-sentence principles and scoped skill sentences throughout extraction, storage, and injection.
No principles	Constrain Extract to return an empty principle set; retrieve and inject skills only.
Single document	Replace itemized banks with one principle document and one skill document, updated through section-level add/replace/delete operations and injected in full. This also removes item-level retrieval.
Inject all	Remove task-conditioned ranking and inject every stored item in reverse creation order.
Batch of four	Buffer four completed tasks and perform one cross-task reflection; use one validation rollout per validation task.
All tasks	Apply Extract and Validate after all-pass and all-fail tasks as well as mixed tasks; the 100-step horizon consequently counts raw tasks.

Appendix

Table 6: Method-level definitions of the ablations.

Component	Prior work with our choice	Prior work with the ablated choice
Retrieval Selection $\to$ inject all	Voyager ( Wang et al., 2023 ) , ReasoningBank ( Ouyang et al., 2025 ) , Memp ( Fang et al., 2025 )	AWM ( Wang et al., 2025b ) , ExpeL ( Zhao et al., 2024 ) , SkillOpt ( Yang et al., 2026 )
Training stream Contrastive only $\to$ all tasks	ExpeL ( Zhao et al., 2024 ) , H 2 R ( Ye et al., 2025 ) , Training-Free GRPO ( Cai et al., 2025 )	ReasoningBank ( Ouyang et al., 2025 ) , ACE ( Zhang et al., 2026 ) , SkillOpt ( Yang et al., 2026 )
Principle memory Remove principles	MARS ( Hou et al., 2026 ) , SkillRL ( Xia et al., 2026 ) , H 2 R ( Ye et al., 2025 )	Voyager ( Wang et al., 2023 ) , AWM ( Wang et al., 2025b )
Skill format Rich markdown $\to$ sentence	Trace2Skill ( Ni et al., 2026 ) , EvoSkill ( Alzubi et al., 2026 ) , SkillOpt ( Yang et al., 2026 )	ExpeL ( Zhao et al., 2024 ) , ACE ( Zhang et al., 2026 ) , Training-Free GRPO ( Cai et al., 2025 )
Skill storage Bank $\to$ single document	SkillRL ( Xia et al., 2026 ) , ReasoningBank ( Ouyang et al., 2025 ) , SWE-Exp ( Chen et al., 2025 )	SkillOpt ( Yang et al., 2026 ) , ACE ( Zhang et al., 2026 ) , Dynamic Cheatsheet ( Suzgun et al., 2025 )
Generation granularity Batch size $1\to 4$	ReasoningBank ( Ouyang et al., 2025 ) , ACE ( Zhang et al., 2026 ) , Dynamic Cheatsheet ( Suzgun et al., 2025 )	SkillOpt ( Yang et al., 2026 ) , Trace2Skill ( Ni et al., 2026 ) , Training-Free GRPO ( Cai et al., 2025 )

Appendix

Table 7: Prior self-evolving methods that adopt our choice or the ablated choice for each component in Figure 3 .

		Train+validation wall-clock time (h)
Model	Setting	pygments	sqlfluff	deepdiff	pydicom	Model Avg.
MiniMax-M2.7	SkillOpt	$118.5$	$134.1$	$105.7$	$121.7$	$120.0$
	Evolve w/o val	$42.9$	$46.3$	$47.2$	$73.9$	$52.6$
	Evolve w/ val	$127.5$	$107.8$	$139.0$	$153.5$	$131.9$
GPT-5.5	SkillOpt	$131.9$	$140.5$	$90.5$	$132.6$	$123.9$
	Evolve w/o val	$40.1$	$97.6$	$55.5$	$67.3$	$65.1$

Appendix

Table 8: Total wall-clock runtime in hours for the main SWE-smith experiments, with held-out test evaluation excluded. The Model Avg. column averages repositories within each model, and the Repo average block averages models within each repository. MiniMax-M2.7 is served on local GPUs, so its times also reflect local inference throughput.

ID	Component	Configuration	Hours / step
Ref.	Full pipeline	Mixed-only, validate $50$	$2.11$
A1	Skill format	Sentence	$2.41$
A2	Principle memory	No principles	$2.20$
A3	Storage	Single document	$2.08$
A4	Selection	Inject all	$1.83$
A5	Extraction batch	Batch size $4$	$1.58$

Appendix

Table 9: Wall-clock runtime per evolution step for the GPT-5.5 pydicom component ablations. The reference configuration is shaded. Only per-step time is reported; total runtime and the number of evolved steps are omitted.

Steps	Item	Type	Category	Gain
1	scope_reported_vs_scope_tested	Principle	Repair strategy	$+5.33$
1	diff_from_baseline_before_fixing	Principle	Repair strategy	$+9.00$
1	audit_full_modified_file_for_multiple_bugs	Skill	Repair strategy	$+2.00$
2	partial_fix_can_fail_tests_that_full_fix_passes	Principle	Repair strategy	$+5.00$
2	co_introduced_bugs_cluster_in_same_commit	Principle	Repair strategy	$+7.00$
2	verify_fix_completeness_with_full_test_suite	Skill	Repair strategy	$+2.33$

Appendix

Table 10: Single-item utility for all items in the final Claude-4.6 pygments bank, listed in learning order. Gain is measured relative to the no-memory baseline on the

100

-task holdout.

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