新闻
Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- SAGE framework learns lightweight RL policies from vision-language models by selectively querying the VLM only when uncertain and weighting its advice by environment rewards.
- 为何重要
- Relevant for engineers building autonomous agents that need cheap inference at deployment while leveraging expensive VLM guidance during training on visual reasoning and navigation tasks.
- 注意
- Selective guidance helps most when VLMs aid high-reward discovery; it provides little benefit when unguided exploration already succeeds or teacher actions lack informative value.
- agent
- language model
- distill
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