新闻
Mismatch Matters: On-Policy Distillation Beyond Token Agreement
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers identified a failure mode in on-policy distillation where student models achieve token agreement with teachers while producing globally flawed responses, and proposed TIDE to address token-level mismatches.
- 为何重要
- Engineers building LLM post-training pipelines should care when training smaller models to mimic larger ones, especially in mathematical reasoning tasks.
- 注意
- TIDE was tested only on Qwen3 teacher-student pairs and mathematical reasoning benchmarks; generalization to other domains and model families remains unclear.
收听本摘要
- llm
- post-train
- distill
- token
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