@inproceedings{deng-etal-2025-curr,
title = "Curr-{R}e{FT}: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning",
author = "Deng, Huilin and
Zou, Ding and
Zhao, Xinghao and
Ma, Rui and
Guo, Yanming and
Cao, Yang and
Kang, Yu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.643/",
doi = "10.18653/v1/2025.findings-emnlp.643",
pages = "12021--12032",
ISBN = "979-8-89176-335-7",
abstract = "State-of-the-art vision-language models (VLMs) require massive scaling that limits practical deployment. Small-scale VLMs offer a practical alternative but face out-of-domain (OOD) collapse when trained with traditional supervised fine-tuning (SFT). Through GeneralPoints experiments, we identify that OOD collapse is due to SFT{'}s tendency to induce visual hallucinations under distribution shifts, whereas Reinforcement Learning{'}s (RL) bidirectional reward-driven mechanism with iterative error correction refines visual perception. Although RL-based post-training effectively mitigates OOD degradation, it faces a critical sparse reward dilemma in complex visual reasoning tasks. To this end, we propose Curriculum Reinforcement Finetuning (Curr-ReFT), comprising two sequential stages: (1) Structured Curriculum Reinforcement Learning, which progressively evolves task formats and reward functions to match models' growing capabilities; and (2) Rejected Sampling-based Self-improvement, which maintains the fundamental capabilities of VLMs through selective learning from high-quality examples. Extensive experiments demonstrate that Curr-ReFT achieves state-of-the-art performance across various visual tasks in both in- and out-of-domain settings and benchmarks."
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<abstract>State-of-the-art vision-language models (VLMs) require massive scaling that limits practical deployment. Small-scale VLMs offer a practical alternative but face out-of-domain (OOD) collapse when trained with traditional supervised fine-tuning (SFT). Through GeneralPoints experiments, we identify that OOD collapse is due to SFT’s tendency to induce visual hallucinations under distribution shifts, whereas Reinforcement Learning’s (RL) bidirectional reward-driven mechanism with iterative error correction refines visual perception. Although RL-based post-training effectively mitigates OOD degradation, it faces a critical sparse reward dilemma in complex visual reasoning tasks. To this end, we propose Curriculum Reinforcement Finetuning (Curr-ReFT), comprising two sequential stages: (1) Structured Curriculum Reinforcement Learning, which progressively evolves task formats and reward functions to match models’ growing capabilities; and (2) Rejected Sampling-based Self-improvement, which maintains the fundamental capabilities of VLMs through selective learning from high-quality examples. Extensive experiments demonstrate that Curr-ReFT achieves state-of-the-art performance across various visual tasks in both in- and out-of-domain settings and benchmarks.</abstract>
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%0 Conference Proceedings
%T Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning
%A Deng, Huilin
%A Zou, Ding
%A Zhao, Xinghao
%A Ma, Rui
%A Guo, Yanming
%A Cao, Yang
%A Kang, Yu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F deng-etal-2025-curr
%X State-of-the-art vision-language models (VLMs) require massive scaling that limits practical deployment. Small-scale VLMs offer a practical alternative but face out-of-domain (OOD) collapse when trained with traditional supervised fine-tuning (SFT). Through GeneralPoints experiments, we identify that OOD collapse is due to SFT’s tendency to induce visual hallucinations under distribution shifts, whereas Reinforcement Learning’s (RL) bidirectional reward-driven mechanism with iterative error correction refines visual perception. Although RL-based post-training effectively mitigates OOD degradation, it faces a critical sparse reward dilemma in complex visual reasoning tasks. To this end, we propose Curriculum Reinforcement Finetuning (Curr-ReFT), comprising two sequential stages: (1) Structured Curriculum Reinforcement Learning, which progressively evolves task formats and reward functions to match models’ growing capabilities; and (2) Rejected Sampling-based Self-improvement, which maintains the fundamental capabilities of VLMs through selective learning from high-quality examples. Extensive experiments demonstrate that Curr-ReFT achieves state-of-the-art performance across various visual tasks in both in- and out-of-domain settings and benchmarks.
%R 10.18653/v1/2025.findings-emnlp.643
%U https://aclanthology.org/2025.findings-emnlp.643/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.643
%P 12021-12032
Markdown (Informal)
[Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning](https://aclanthology.org/2025.findings-emnlp.643/) (Deng et al., Findings 2025)
ACL