Partial Reasoning Traces More Effective Than Complete Trajectories for SFT
September 6, 2026
Research shows that providing complete, complex reasoning trajectories during supervised fine-tuning offers limited benefits compared to partial trajectories. Analysis suggests that intermediate tokens in long reasoning paths often contribute minimally to final output quality.
HOW THIS AFFECTS YOU
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builderThis suggests a more cost-effective way to prepare fine-tuning datasets for reasoning-heavy models.
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researcherYou can optimize training efficiency by using truncated, high-signal reasoning steps instead of full, redundant trajectories.