inplace-feedback-multi-turn-reasoning

【效率与协作】Enable more precise LLM error correction by having users directly edit the model's previous response, conditioning the next response on this corrected version. This approach reduces token overhead by 79% compared to traditional separate-feedback methods while fixing more errors in complex reasoning tasks.

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