Tree-Ising repairs Qwen reasoning loops with sparse BF16 edits
On two layers it scores 54.6% on MATH-500 versus 51.7% for a GPT-6 Astra edit and 7.9% for the base model.
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On two layers it scores 54.6% on MATH-500 versus 51.7% for a GPT-6 Astra edit and 7.9% for the base model.
The pipeline cut false-positive verdicts on natural-language proofs from 23% to 6% versus one-shot LLM judgment.
LASEF finds English reasoning skeletons help smaller models slightly, but language choice is context-dependent.
Study of on-policy self-distillation on 5,319 AMPLE-Math problems finds reference-free distillation drives most gains, with modest extra benefit from privileged references.
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