Paper argues the middle of a reasoning trace can be dropped when fine-tuning

A September 2026 research paper asks whether language models actually benefit from training on complete step-by-step reasoning examples, a question the authors say has been underexplored. The authors, Jaehui Hwang, Sangdoo Yun, Byeongho Heo and Dongyoon Han, submitted "Revisiting Complete Reasoning Traces for Post-Training" to arXiv on 7 September 2026.

The paper concerns supervised fine-tuning (SFT) on pre-collected reasoning trajectories. The authors note that these trajectories are often long because they follow interwoven paths and include detours before the answer. Their pilot study, they say, found that full trajectories give only limited benefit, while partial ones stay effective even when heavily truncated.

To explain this, the authors report attention-based analyses and controlled token-removal studies. They say both show that the intermediate tokens contribute little to final reasoning quality. They suggest a model may be able to infer the missing steps from its own knowledge when it is shown where a trajectory begins and ends. That is presented as an interpretation, not a demonstrated mechanism.

From this, they propose endpoint-based SFT, or E-SFT, which trains on only the beginning and ending parts of each trace. The authors say E-SFT retains or even improves SFT performance. They also report that endpoint training changes reasoning behavior and helps methods built on reinforcement learning, such as GRPO, and on-policy distillation, or OPD.

What is not confirmed here is substantial. The abstract pages do not state which models, datasets or benchmarks were used, how much of each trace was removed, or how large any improvement was. Both sources are the authors' own abstract, one on arXiv and one on a Hugging Face paper page, so neither independently verifies the results. We found no independent coverage or replication. The arXiv listing says the paper is to appear in EMNLP 2026 Findings, but acceptance at a venue is not independent replication, and the details of the results are in the full paper, which we have not reviewed. Everything above is the authors' account.

The finding matters because long reasoning traces are expensive to generate, store and train on. If the middle of a trace adds little, builders could test shorter examples as a cheaper alternative. The abstract does not show that this holds across models, tasks or training setups, so the practical value depends on the full paper's experiments, which we have not reviewed.

Revised 16 October 2026: Guth re-read the original sources and rewrote this article. The earlier version remains available as revision 1.