Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation
- 类型：arxiv
- 标识：2609.13770
- 链接：https://arxiv.org/abs/2609.13770
- 主分类：engineering
- 形态：method
- TLDR：Specialist distillation effectively transfers domain expertise to student models via teacher-generated reasoning trajectories. However, when these specialists are trained solely on question--answer pairs without explicit reasoning supervision, what governs the trajectories they generate? In this work, we show that specialist optimization implicitly selects from this latent trajectory space. To isolate and observe this latent distribution, we leverage student distillation not as a downstream goal, but as an agnostic probe---since students inherit no parameterization or optimization constraints
- 待LLM分类：否
- 来源文件：
- /inbox/tom/_candidates/2026-09-16-agent-rag-longcontext-candidates.json