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May 1, 2026Open Access

Verification Collapse in Iterative Self-Improving Language Models

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Authors

LGLeonardo Giuseppe Gianola

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Overview

Randomized trial investigates self-evaluation accuracy in self-improving language models, revealing verification collapse.

Key Points

  • This research aims to investigate the stability of self-evaluation in self-improving language models over multiple training iterations.
  • Analyzed self-evaluation accuracy across 20 iterations using the MATH benchmark with three model families.
  • Introduced adversarial hard-negative injection to mitigate the verification gap.
  • Compared a ground-truth-based control to isolate the effects of self-scoring.
  • Verification gap increased across all models with Qwen2.5-7B showing Δ_1 = 0.173 and Δ_20 = 0.320.
  • Adversarial hard-negative injection reduced the verification gap by 47% on Qwen2.5-7B.
  • Control with ground-truth filtering eliminated collapse entirely, confirming self-scoring as the causal driver.

Cite This Study

Leonardo Giuseppe Gianola (2026) studied this question.

synapsesocial.com/papers/69f443e8967e944ac5566fadhttps://doi.org/10.5281/zenodo.19890194
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Verifier Gap: Negative Scaling and Syntactic-Logical Divergence in Reasoning Distillation2026
  2. 2Uncertainty Collapse in Post-Trained Language Models: Keep Calm or Carry On2026
  3. 3Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models2026 · 1 citations
  4. 4The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback2025
  5. 5When Models Don't Collapse: On the Consistency of Iterative MLE2025