arXiv (OAI Expanded)
open access
Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation
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 wor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging#1116634
- Beyond Imitation: Reflective On-Policy Self-Distillation for LLM Reasoning#1129277
- TV-Regulated OPD: Direction Matters in On-Policy Distillation#985701
- OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation#924089
- OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation#919860
Record · ID 1083271
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive