Study Questions Whether Stage-1 Pre-training Controls Vision-Language Model Outcomes
Researchers analyzing two-stage post-training for vision-language models found that Stage-1 (initial fine-tuning) primarily controls the entropy regime—the diversity of model outputs—rather than final performance. The study used Qwen2.5-VL-7B with different warm-start methods including supervised fine-tuning and on-policy distillation. The findings suggest Stage-1's practical impact on downstream task performance is small and localized, challenging assumptions about its role in model training.
A new arXiv paper examines what the first stage of two-stage post-training actually controls in vision-language models, using Qwen2.5-VL-7B as a test case. Researchers compared three warm-start approaches and found that all three converged to a narrow 53-54% performance band on internal validation, with little evidence that Stage-1 meaningfully changes in-domain endpoints. The clearest distinction was in entropy regime: on-policy distillation entered reinforcement learning with substantially higher policy entropy than supervised fine-tuning approaches. While on-policy distillation showed higher answer diversity and pass@16 scores during training (+2.0 to +5.2 points), these advantages disappeared after RL training and on out-of-domain benchmarks like MathVista. The authors conclude that Stage-1 is strongly associated with entropy regime but provides only small, localized downstream benefits, questioning whether on-policy distillation is genuinely superior as an RL warm-start.
What's missing
The study is limited to a single model architecture (Qwen2.5-VL-7B) and a small-data regime; generalizability to other VLMs, larger datasets, or different task domains remains unclear. The authors note that problem-level bootstrap intervals show smaller contrasts are uncertain, and they do not provide analysis of computational costs or training efficiency differences between the warm-start methods.
What different sources said
- arXiv cs.AICenter
Stage-1 Controls the Entropy Regime, Not the Outcome
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