arXiv:2605.29288v1 Announce Type: new Abstract: Long chain-of-thought (CoT) traces are widely used as supervision for reasoning-oriented LLM SFT, yet answer-correct traces can still lead to markedly different fine-tuning outcomes. We study post-conclusion continuation in answer-correct long-CoT data: a continuation where the answer appears sufficiently supported, but the trace continues with additional reasoning that remains in the supervised target. To test its training effect, we use a delete-only editor to construct answer-preserving suffix removal and compare CoT-based SFT on the original
The proliferation of complex LLM applications and the increasing sophistication of training methodologies necessitate a deeper understanding of how subtle data characteristics influence model behavior.
This research reveals critical nuances in training data effectiveness for LLMs, demonstrating that 'correctness' alone is insufficient and hidden patterns can significantly alter performance.
The understanding of what constitutes optimal training data for reasoning-oriented LLMs shifts, emphasizing the need for meticulous data curation beyond simple answer verification.
- · LLM researchers
- · Data scientists
- · AI developers focused on reasoning
- · Model explainability platforms
- · Developers using naive CoT datasets
- · LLM projects with poor data curation
- · AI models suffering from 'harmful continuation'
Refined data curation practices will emerge for large language model (LLM) training, specifically for chain-of-thought (CoT) applications.
New tools and methodologies will be developed to identify and mitigate 'harmful continuation' and similar subtle data quality issues in instruction tuning datasets.
The overall robustness and reliability of reasoning-oriented LLMs will improve, leading to more trustworthy AI agents capable of complex decision-making.
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Read at arXiv cs.AI