Gouki Minegishi
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126
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AI×1ML×1
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2026
Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
This paper investigates how different types of compressed reasoning data (Explicit, Composed, Implicit CoT) affect LLM performance during post-training, finding that the choice of compression and subsequent fine-tuning method significantly impacts generalization and data scaling.
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