新闻
There Is No Free Loop: When Does Looping a Trained Model Pay for Its Inference Compute?
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers found that adding computational loops to pretrained models only improves performance for small models, not larger ones, despite keeping parameters constant.
- 为何重要
- Matters for engineers optimizing inference costs on fixed parameter budgets, especially when considering architectural modifications to deployed models.
- 注意
- Results tested only on Qwen3 and Qwen3.5 models; generalization to other architectures and whether loops learned during pretraining behave differently remains unclear.
- inference
- token
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