PROGRESSLM: Towards Progress Reasoning in Vision-Language Models
Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Language Models (VLMs) excel at describing what is visible, it remains unclear whether they can infer how far a task has progressed from partial observations. To this end, we introduce Progress-Bench, a benchmark for systematically evaluating progress reasoning in VLMs. Beyond benchmarking, we further explore a human-inspired two-stage progress reasoning paradigm through both training-free prompting and training-based approach based on curated dataset ProgressLM-45K. Experiments on 14 VLMs show that most models are not yet ready for task progress estimation, exhibiting sensitivity to demonstration modality and viewpoint changes, as well as poor handling of unanswerable cases. While training-free prompting that enforces structured progress reasoning yields limited and model-dependent gains, the training-based ProgressLM-3B achieves consistent improvements even at a small model scale, despite being trained on a task set fully disjoint from the evaluation tasks. Further analyses reveal characteristic error patterns and clarify when and why progress reasoning succeeds or fails.
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Jianshu Zhang
69730faffb12c92b735b7551
Chengxuan Qian
claimed_verified
2026-01-23T09:38:16.626Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/67f5e63688b2c5303ab5be7a/NU9m1iPKcCagYoiblh6w1.jpeg
Chengxuan Qian
Raymond-Qiancx
user
Raymond-Qiancx
69730faffb12c92b735b7552
Haosen Sun
69730faffb12c92b735b7553
Haoran Lu
69730faffb12c92b735b7554
Dingcheng Wang
69730faffb12c92b735b7555
Letian Xue
69730faffb12c92b735b7556
Han Liu
PROGRESSLM: Towards Progress Reasoning in Vision-Language Models
Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Language Models (VLMs) excel at describing what is visible, it remains unclear whether they can infer how far a task has progressed from partial observations. To this end, we introduce Progress-Bench, a benchmark for systematically evaluating progress reasoning in VLMs. Beyond benchmarking, we further explore a human-inspired two-stage progress reasoning paradigm through both training-free prompting and training-based approach based on curated dataset ProgressLM-45K. Experiments on 14 VLMs show that most models are not yet ready for task progress estimation, exhibiting sensitivity to demonstration modality and viewpoint changes, as well as poor handling of unanswerable cases. While training-free prompting that enforces structured progress reasoning yields limited and model-dependent gains, the training-based ProgressLM-3B achieves consistent improvements even at a small model scale, despite being trained on a task set fully disjoint from the evaluation tasks. Further analyses reveal characteristic error patterns and clarify when and why progress reasoning succeeds or fails.
Vision-Language Models struggle with task progress estimation from partial observations, requiring new approaches like ProgressLM-45K and ProgressLM-3B to improve reasoning capabilities.
Vision-Language Models progress reasoning Progress-Bench ProgressLM-45K ProgressLM-3B training-free prompting training-based approach