Scaffold-Mediated Post-Training: Co-Evolving Model Parameters and Procedural Scaffold Graphs
arXiv:2608.05156v1 Announce Type: new Abstract: Post-training of large language models optimizes only parameters, while inference-time procedural scaffolds are typically designed independently of parameter training. This disconnect makes it difficult to automatically acquire and internalize complex strategies. We propose scaffold-mediated post-training: procedural scaffolds are organized into an evolvable graph structure that co-evolves with model parameters through discovery, distillation, and dynamic recompilation. We instantiate this paradigm as Skill Training. On FeatureBench, automatically discovered skills improve the passed rate by 8.1pp, and after progressive distillation the model still achieves a 27.7% passed rate without any external scaffold (distillation retention rate 85.2%, defined as post-distillation / with-skill passed rate), significantly outperforming standard SFT on the same data.
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[Submitted on 22 May 2026]
Title:Scaffold-Mediated Post-Training: Co-Evolving Model Parameters and Procedural Scaffold Graphs
View a PDF of the paper titled Scaffold-Mediated Post-Training: Co-Evolving Model Parameters and Procedural Scaffold Graphs, by Fei Ding and 5 other authors
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Abstract:Post-training of large language models optimizes only parameters, while inference-time procedural scaffolds are typically designed independently of parameter training. This disconnect makes it difficult to automatically acquire and internalize complex strategies. We propose scaffold-mediated post-training: procedural scaffolds are organized into an evolvable graph structure that co-evolves with model parameters through discovery, distillation, and dynamic recompilation. We instantiate this paradigm as Skill Training. On FeatureBench, automatically discovered skills improve the passed rate by 8.1pp, and after progressive distillation the model still achieves a 27.7% passed rate without any external scaffold (distillation retention rate 85.2%, defined as post-distillation / with-skill passed rate), significantly outperforming standard SFT on the same data.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2608.05156 [cs.CL]
(or arXiv:2608.05156v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.05156
arXiv-issued DOI via DataCite
Submission history
From: H.M Yang [view email] [v1] Fri, 22 May 2026 18:45:53 UTC (39 KB)
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