STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution
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Computer Science > Computation and Language
arXiv:2609.18642 (cs)
[Submitted on 16 Sep 2026]
Title:STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution
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Abstract:Large language models (LLMs) often suffer from capability stagnation in self-improvement training because fixed difficulty levels fail to adapt to their evolving proficiency. To address this issue, we propose STRETCH (Self-Taught Reasoning Evolution via Targeted CHallenge), a unified framework inspired by cognitive scaffolding theory. STRETCH introduces a dynamic Stretch Zone mechanism that continuously aligns question difficulty with the model's solving capability. Within a single parameter space, the model alternates between a Scaffolder that generates adaptive, boundary-pushing challenges and a Learner that that optimizes its solving trajectories through reinforcement learning. This dual-loop co-evolution effectively stabilizes training, mitigates reward hacking and promote progressive reasoning growth. Experiments on both negotiation and operation research benchmarks demonstrate that STRETCH consistently outperforms strong prompting and domain-specific baselines. Further scaffolder configuration analysis shows that dynamic difficulty alignment is critical for sustained capability improvement and synchronized reasoning evolution.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.18642 [cs.CL] |
| (or arXiv:2609.18642v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.18642
arXiv-issued DOI via DataCite (pending registration)
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