Kohsei Matsutani
1 indexed paper
Recent (6 mo)
1With code
0Influential cites
0Benchmarked
0Publications per year
126
Top categories
AI×1ML×1
Frequent co-authors
Research Timeline
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.
Highlighted terms show continued research focus across papers