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AI Research
arXiv CS.AI·Jun 9·5 min read
Stage-1 Controls the Entropy Regime, Not the Outcome
TL;DR
A gap exists in understanding how Stage-1 warm-start methods influence the performance of vision-language models (VLMs). The study utilized Qwen2.5-VL-7B with a 72B VLM teacher for on-policy distillation (OPD) to analyze this influence.
✦ Why It Matters
Engineers can refine their model training strategies by understanding the limited impact of Stage-1 warm-start methods on final performance.
Key Takeaways
- Explore different warm-start methods for VLMs to enhance training diversity.
- Monitor policy entropy during reinforcement learning for better outcomes.
- Utilize early-stopped SFT to improve out-of-domain performance.
- Recognize that Stage-1 primarily affects entropy, not final performance.
How It Works
The study compares warm-start methods by analyzing their effects on policy entropy and answer diversity during reinforcement learning. OPD, for instance, enters RL with higher entropy, which may lead to more diverse responses.