Jackrong/claude-opus-4.6-traceInversion-9000x

Summary

⚠️ 1.1 The Distillation Dilemma of "Reasoning Bubbles" Currently, LLM distillation is entering deep waters. However, the concealment of internal thinking chains by state-of-the-art commercial models leads to severe information loss during the distillation of community-driven small models. Although the short summaries provided by commercial black boxes look clean and concise, in highly difficult logic scenarios such as mathematical derivations and complex code generation, the lack of intermediate proof steps causes models to learn "logical fractures" when these summaries are used directly as SFT supervision signals.

Keywords

hf-dataset language-modeling · -reasoning machine-generated json text datasets pandas polars mlcroissant reasoning trace-inversion synthetic-data chain-of-thought distillation claude-opus negentropy qwen unsloth has-paper

Topics

Language Modeling, Reasoning

Research notes

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