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Reddit users question fine-tuning LLMs on summarized reasoning traces

A discussion on Reddit questions the practice of fine-tuning large language models on summarized or censored Chain-of-Thought (CoT) traces. The user posits that this method, particularly with models like Anthropic's Fable fine-tunes, may not improve output quality and could even degrade it. This is because the provided reasoning traces might not accurately reflect the model's internal thought process, leading to suboptimal results. AI

IMPACT This discussion raises questions about the effectiveness of certain fine-tuning techniques for large language models.

RANK_REASON The item is a discussion on Reddit questioning a specific AI training methodology.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reddit users question fine-tuning LLMs on summarized reasoning traces

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The item is a discussion on Reddit questioning a specific AI training methodology.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/wombweed ·

    Why do people keep fine-tuning on summarized/censored SOTA CoT traces?

    <!-- SC_OFF --><div class="md"><p>Am I missing something? It seems like some people think distillation is magic and will raise the quality of output above what the base model is actually capable of. It's especially weird to me to see all these Fable fine-tunes, because as far as …