DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation
The paper titled 'DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation' addresses inefficiencies in the training of Vision-Language Models. It highlights that a significant portion of prompts used in standard datasets are ineffective due to minimal learning signals. The authors propose a new dataset, DeltaPrompts, which aims to improve model performance by focusing on prompts that reveal functional capability gaps.
- ▪Up to 69% of prompts in standard reasoning datasets are effectively zero-delta, providing minimal learning signal.
- ▪The proposed DeltaPrompts dataset consists of 200k synthetic, high-divergence reasoning problems.
- ▪DeltaPrompts yields up to 15% relative improvement on various benchmarks, even with highly-optimized reasoning models.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15532 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Machine Learning arXiv:2605.15532 (cs) [Submitted on 15 May 2026] Title:DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation Authors:Jaehun Jung, Hyunwoo Kim, Brandon Cui, Ximing Lu, David Acuna, Prithviraj Ammanabrolu, Yejin Choi View a PDF of the paper titled DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation, by Jaehun Jung and 6 other authors View PDF HTML (experimental) Abstract:Distillation enables compact Vision-Language Models (VLMs) to obtain strong reasoning capabilities, yet the prompts driving this process are typically chosen via simple heuristics or aggregated from off-the-shelf datasets.
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