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This study investigates the effects of post-conclusion continuation in answer-correct long chain-of-thought (CoT) training traces for reasoning-oriented LLMs. By employing a delete-only editor to remove harmful continuations while preserving correct answers, the authors demonstrate that eliminating these continuations leads to significantly improved fine-tuning outcomes. The findings reveal a critical phenomenon termed "harmful continuation," characterized by persistent local uncertainty and weakened terminal-directional progress, which can negatively impact model training.
Removing misleading reasoning from answer-correct CoT traces can drastically enhance fine-tuning performance in LLMs.
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 and processed traces. We observe improved SFT outcomes after removing the editor-identified post-conclusion continuation, suggesting that this continuation is harmful to training in our setting. We therefore refer to this empirically supported phenomenon as harmful continuation. Beyond this intervention, we further characterize the removed post-conclusion continuation through uncertainty and hidden-state progress. We observe persistent local uncertainty together with weakened terminal-directional progress, forming an uncertainty--geometry mismatch. Finally, we instantiate Harmful Continuation Cut (HCC), a lightweight boundary proxy that approximates the editor-identified post-conclusion continuation boundary.
